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  • 1.
    Andersson, Ewa K.
    et al.
    Linnaeus University.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Marcinowicz, Ludmila
    Medical University of Bialystok, Poland.
    Stjernberg, Louise
    Malmö University.
    Björling, Gunilla
    Jönköping University.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Bohman, Doris
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Self-Reported eHealth literacy among nursing students in Sweden and Poland: The eNursEd cross-sectional multicentre study2023In: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 29, no 4Article in journal (Refereed)
    Abstract [en]

    This study aimed to provide an understanding of nursing students’ self-reported eHealth literacy in Sweden and Poland. This cross-sectional multicentre study collected data via a questionnaire in three universities in Sweden and Poland. Descriptive statistics, the Spearman’s Rank Correlation Coefficient, Mann–Whitney U, and Kruskal–Wallis tests were used to analyse different data types. Age (in the Polish sample), semester, perceived computer or laptop skills, and frequency of health-related Internet searches were associated with eHealth literacy. No gender differences were evidenced in regard to the eHealth literacy. Regarding attitudes about eHealth, students generally agreed on the importance of eHealth and technical aspects of their education. The importance of integrating eHealth literacy skills in the curricula and the need to encourage the improvement of these skills for both students and personnel are highlighted, as is the importance of identifying students with lacking computer skills. © The Author(s) 2023.

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  • 2.
    Berner, Jessica
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Five-factor model, technology enthusiasm and technology anxiety2023In: Digital Health, E-ISSN 2055-2076, Vol. 9Article in journal (Refereed)
    Abstract [en]

    Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online. © The Author(s) 2023.

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  • 3.
    Berner, Jessica
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Technology anxiety and technology enthusiasm versus digital ageism2022In: Gerontechnology, ISSN 1569-1101, E-ISSN 1569-111X, Vol. 21, no 1Article in journal (Refereed)
    Abstract [en]

    Background: Europe has called attention to the importance of the e-inclusion of older adults. Society is indicating that the developers, websites, and devices are causing age bias in technology. This affects living independently, the values of ethical principles associated with an older person, and digital ageism: which is an age-related bias in artificial intelligence systems. Objective: This research attempts to investigate the instrument technology anxiety and enthusiasm, and assistive technology devices during the period 2019- 2021. This instrument may be a way to redress misconceptions about digital ageism. The assistive technology device that we will investigate in this study is the adoption of a service that is designed for online health consultations. Method: The participants are part of the longitudinal Swedish National Study on Aging and Care. Technology anxiety and technology enthusiasm are two factors, which aim to measure technophilia (vs technophobia) in older adults. The age range is 63 -99 years of age in 2019 T1 and 66 -101 in 2021 T2. Wilcoxon rank test was conducted to investigate technology enthusiasm, technology anxiety, and how they changed with time. An Edwards Nunnally index was then calculated for both variables to observe a significant change in score from T1 to T2. Mann Whitney U test was used to investigate the variables sex and health status with technology anxiety & technology enthusiasm in T1 & T2. Age, Cognitive function MMSE, and digital social participation were investigated through a Kruskall-Wallis test. A logistic regression was conducted with the significant variable. Results: Between 2019-2021, change in technology enthusiasm was based on less digital social participation (OR: 0.608; CI 95%: 0.476- 0.792). Technology anxiety was significantly higher due to age (OR: 1.086, CI 95%: 1.035-1.139) and less digital social participation (OR: 0.684; CI 95%: 0.522- 0.895). The want for online healthcare consultations was popular but usage was low. Conclusion: Staying active on- line and participating digitally may be a way to reduce digital ageism. However, digital ageism is a complex phenomenon, which requires different solutions in order to include older people and reduce an inaccurate categorisation of this group in the digital society.

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  • 4.
    Dallora Moraes, Ana Luiza
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Supplementary Material of: “Prognosis of dementia with machine learning and microssimulation techniques: a systematic literature review”.2016Other (Other academic)
    Abstract [en]

     This document contains the supplementary material regarding the systematic literature review entitled: “Prognosis of dementia with machine learning and microssimulation techniques: a systematic literature review”.

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  • 5.
    Dallora Moraes, Ana Luiza
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Systematic Literature Review Protocol:  Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review2016Other (Other academic)
    Abstract [en]

     This document contains the protocol followed to conduct the systematic literature review entitled: “Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review”.

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  • 6.
    Dallora Moraes, Ana Luiza
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Kvist, Ola
    KI, SWE.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ruiz, Sandra
    KI, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis2019In: PLOS ONE, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article, review/survey (Refereed)
    Abstract [en]

    Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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  • 7.
    Dallora Moraes, Ana Luiza
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Brogren, Martin
    Optriva AB, SWE.
    Kvist, Ola
    Karolinska, SWE.
    Ruiz, Sandra Diaz
    Karolinska, SWE.
    Dübbel, André
    Optriva AB, SWE.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Age assessment of youth and young adults using magnetic resonance imaging of the knee: A deep learning approach2019In: JMIR Medical Informatics, E-ISSN 2291-9694, Vol. 7, no 4, p. 419-436, article id e16291Article in journal (Refereed)
    Abstract [en]

    Background: Bone age assessment (BAA) is an important tool for diagnosis and in determining the time of treatment in a number of pediatric clinical scenarios, as well as in legal settings where it is used to estimate the chronological age of an individual where valid documents are lacking. Traditional methods for BAA suffer from drawbacks, such as exposing juveniles to radiation, intra- and interrater variability, and the time spent on the assessment. The employment of automated methods such as deep learning and the use of magnetic resonance imaging (MRI) can address these drawbacks and improve the assessment of age. Objective: The aim of this paper is to propose an automated approach for age assessment of youth and young adults in the age range when the length growth ceases and growth zones are closed (14-21 years of age) by employing deep learning using MRI of the knee. Methods: This study carried out MRI examinations of the knee of 402 volunteer subjects-221 males (55.0%) and 181 (45.0%) females-aged 14-21 years. The method comprised two convolutional neural network (CNN) models: the first one selected the most informative images of an MRI sequence, concerning age-assessment purposes; these were then used in the second module, which was responsible for the age estimation. Different CNN architectures were tested, both training from scratch and employing transfer learning. Results: The CNN architecture that provided the best results was GoogLeNet pretrained on the ImageNet database. The proposed method was able to assess the age of male subjects in the range of 14-20.5 years, with a mean absolute error (MAE) of 0.793 years, and of female subjects in the range of 14-19.5 years, with an MAE of 0.988 years. Regarding the classification of minors-with the threshold of 18 years of age-an accuracy of 98.1% for male subjects and 95.0% for female subjects was achieved. Conclusions: The proposed method was able to assess the age of youth and young adults from 14 to 20.5 years of age for male subjects and 14 to 19.5 years of age for female subjects in a fully automated manner, without the use of ionizing radiation, addressing the drawbacks of traditional methods. © 2019 Journal of Medical Internet Research. All rights reserved.

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    Age assessment of youth and young adults using magnetic resonance imaging of the knee
  • 8.
    Dellkvist, Helen
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Christiansen, Line
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Skär, Lisa
    Kristianstad University.
    The use of a digital life story to support person-centred care of older adults with dementia: A scoping review2024In: Digital Health, E-ISSN 2055-2076, Vol. 10Article, review/survey (Refereed)
    Abstract [en]

    Introduction

    A life story (LS) is a tool healthcare professionals (HCPs) use to help older adults with dementia preserve their identities by sharing their stories. Applied health technology can be considered a niche within welfare technology. Combining technology and nursing, such as using life stories in digital form, may support person-centred care and allow HCPs to see the person behind the disease.

    Objective

    The study's objective was to summarise and describe the use of life stories in digital form in the daily care of older adults with dementia.

    Methods

    A scoping review was conducted in five stages. Database searches were conducted in Cinahl, PubMed, Scopus, Web of Science, and Google Scholar; 31 articles were included. A conventional qualitative content analysis of the collected data was conducted.

    Results

    The qualitative analysis resulted in three categories: (1) benefits for older adults, (2) influence on HCPs’ work, and (3) obstacles to implementing a digital LS in daily care.

    Conclusion

    Older adults with dementia can receive person-centred care through a digital LS based on their wishes. A digital LS can enable symmetric communication and serve as an intergenerational communication tool. It can be used to handle behavioural symptoms. Using a digital LS in the later stages of dementia may differ from using it earlier in dementia. However, it may compensate for weakening abilities in older adults by enhancing social interaction.

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  • 9.
    Ghazi, Sarah Nauman
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Berner, Jesica
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Psychological Health and Digital Social Participation of the Older Adults during the COVID-19 Pandemic in Blekinge, Sweden—An Exploratory Study2022In: International Journal of Environmental Research and Public Health, E-ISSN 1660-4601, Vol. 19, no 6, article id 3711Article in journal (Refereed)
    Abstract [en]

    COVID-19 has affected the psychological health of older adults directly and indirectly through recommendations of social distancing and isolation. Using the internet or digital tools to participate in society, one might mitigate the effects of COVID-19 on psychological health. This study explores the social participation of older adults through internet use as a social platform during COVID-19 and its relationship with various psychological health aspects. In this study, we used the survey as a research method, and we collected data through telephonic interviews; and online and paper-based questionnaires. The results showed an association of digital social participation with age and feeling lack of company. Furthermore, in addition, to the increase in internet use in older adults in Sweden during COVID-19, we conclude that digital social participation is essential to maintain psychological health in older adults.

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  • 10.
    Idrisoglu, Alper
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review2023In: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 25, article id e46105Article, review/survey (Refereed)
    Abstract [en]

    Background: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research. © 2023 Journal of Medical Internet Research. All rights reserved.

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  • 11.
    Idrisoglu, Alper
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Jakobsson, Andreas
    Lunds universitet.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    COPDVD: Automated Classification of Chronic Obstructive Pulmonary Disease on a New Developed and Evaluated Voice DatasetManuscript (preprint) (Other academic)
    Abstract [en]

    AbstractBackground: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.

    Objective: The proposed study aims to: (i) investigate whether the voice features extracted from the vowel "A" phonation carry information that can be predictive of COPD by employing Machine Learning (ML); and (ii) develop a voice dataset based on the evaluation of the features.

    Methods: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel "A" phonation commenced following an information and consent meeting with each participant using the VoiceDiagnistic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations  (SHAP) feature importance measures. 

    Results: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77%, 69%, and 78% on the test set and 93%, 78% and 97% on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82% and AP of 76%. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. 

    Conclusion: This study concludes that the vowel "A" recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. Lastly, we believe that the newly developed voice dataset will be a valuable resource to researchers within the domain.

  • 12.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ghazi, Ahmad Nauman
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Javeed, Asim
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and Machine Learning Models2024Conference paper (Refereed)
    Abstract [en]

    Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K- nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE.

  • 13.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ali, Arif
    University of Science and Technology Bannu, Pakistan.
    Ali, Liaqata
    University of Science and Technology Bannu, Pakistan.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions2023In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 47, no 1, article id 17Article, review/survey (Refereed)
    Abstract [en]

    Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations. © 2023, The Author(s).

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  • 14.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ali, Arif
    University of Science and Technology Bannu, Pakistan.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ali, Liaqat
    University of Science and Technology Bannu, Pakistan.
    Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks2023In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 75, no 2, p. 2491-2508Article in journal (Refereed)
    Abstract [en]

    Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. © 2023 Tech Science Press. All rights reserved.

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  • 15.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning2022In: Life, E-ISSN 2075-1729, Vol. 12, no 7, article id 1097Article in journal (Refereed)
    Abstract [en]

    Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.

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  • 16.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Idrisoglu, Alper
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ali, Liaqat
    University of Science and Technology Bannu, Pakistan.
    Rauf, Hafiz Tayyab
    Staffordshire University, UK.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification2023In: Biomedicines, E-ISSN 2227-9059, Vol. 11, no 2, article id 439Article in journal (Refereed)
    Abstract [en]

    Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.

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  • 17.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Saleem, Muhammad Asim
    Chulalongkorn University, Thailand.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ali, Liaqat
    University Science & Technology Bannu, Pakistan.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning2023In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 8, article id 5188Article in journal (Refereed)
    Abstract [en]

    Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a ?(2) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (?(2)_RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model ?(2)_RF achieved the highest accuracy of 94.59%. The proposed model ?(2)_RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model ?(2)_RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (?(2)).

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  • 18.
    Javeed, Ashir
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Saleem, Muhammad Asim
    Chulalongkorn University, Thailand.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data2023In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, no 1, article id 188Article in journal (Refereed)
    Abstract [en]

    Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. © 2023, The Author(s).

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  • 19.
    Kvist, Ola F. T.
    et al.
    Karolinska Univ Hosp, SWE.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Nilsson, Ola
    Karolinska Inst, SWE.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Flodmark, Carl-Erik
    Lund Univ, SWE.
    Diaz, Sandra
    Karolinska Univ Hosp, SWE.
    Comparison of reliability of magnetic resonance imaging using cartilage and T1-weighted sequences in the assessment of the closure of the growth plates at the knee2020In: Acta Radiologica Open, E-ISSN 2058-4601, Vol. 9, no 9, article id 2058460120962732Article in journal (Refereed)
    Abstract [en]

    Background: Growth development is traditionally evaluated with plain radiographs of the hand and wrist to visualize bone structures using ionizing radiation. Meanwhile, MRI visualizes bone and cartilaginous tissue without radiation exposure. Purpose: To determine the state of growth plate closure of the knee in healthy adolescents and young adults and compare the reliability of staging using cartilage sequences and T1-weighted (T1W) sequence between pediatric and general radiologists. Material and Methods: A prospective, cross-sectional study of MRI of the knee with both cartilage and T1W sequences was performed in 395 male and female healthy subjects aged between 14.0 and 21.5 years old. The growth plate of the femur and the tibia were graded using a modified staging scale by two pediatric and two general radiologists. Femur and tibia were graded separately with both sequences. Results: The intraclass correlation was overall excellent. The inter- and intra-observer agreement for pediatric radiologists on T1W was 82% (kappa = 0.73) and 77% (kappa = 0.65) for the femur and 90% (kappa = 0.82) and 87% (kappa = 0.75) for the tibia. The inter-observer agreement for general radiologists on T1W was 69% (kappa = 0.56) for the femur and 56% (kappa = 0.34) for the tibia. Cohen's kappa coefficient showed a higher inter- and intra-observer agreement for cartilage sequences than for T1W: 93% (kappa = 0.86) and 89% (kappa = 0.79) for the femur and 95% (kappa = 0.90) and 91% (kappa = 0.81) for the tibia. Conclusion: Cartilage sequences are more reliable than T1W sequence in the assessment of the growth plate in adolescents and young adults. Pediatric radiology experience is preferable.

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    Comparison of reliability of magneticresonance imaging using cartilageand T1-weighted sequences in theassessment of the closure of thegrowth plates at the knee
  • 20.
    Kvist, Ola
    et al.
    Karolinska Univ Hosp, SWE.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Nilsson, Ola
    Karolinska Inst, SWE.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Flodmark, Carl-Erik
    Lund Univ, SWE.
    Diaz, Sandra
    Karolinska Univ Hosp, SWE.
    A cross-sectional magnetic resonance imaging study of factors influencing growth plate closure in adolescents and young adults2021In: Acta Paediatrica, ISSN 0803-5253, E-ISSN 1651-2227, Vol. 110, no 4, p. 1249-1256Article in journal (Refereed)
    Abstract [en]

    Aim To assess growth plate fusion by magnetic resonance imaging (MRI) and evaluate the correlation with sex, age, pubertal development, physical activity and BMI. Methods Wrist, knee and ankle of 958 healthy subjects aged 14.0-21.5 years old were examined using MRI and graded by two radiologists. Correlations of growth plate fusion score with age, pubertal development, physical activity and BMI were assessed. Results Complete growth plate fusion occurred in 75%, 85%, 97%, 98%, 98% and 90%, 97%, 95%, 97%, 98% (radius, femur, proximal- and distal tibia and calcaneus) in 17-year-old females and 19-year-old males, respectively. Complete fusion occurs approximately 2 years earlier in girls than in boys. Pubertal development correlated with growth plate fusion score (rho = 0.514-0.598 for the different growth plate sites) but regular physical activity did not. BMI also correlated with growth plate fusion (rho = 0.186-0.384). Stratified logistic regression showed increased odds ratio (OR F: 2.65-8.71; M: 1.71-4.03) for growth plate fusion of obese or overweight subects versus normal-weight subjects. Inter-observer agreement was high (Kappa = 0.87-0.94). Conclusion Growth plate fusion can be assessed by MRI; occurs in an ascending order, from the foot to the wrist; and is significantly influenced by sex, pubertal development and BMI, but not by physical activity.

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  • 21.
    Moraes, Ana Louiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Eivazzadeh, Shahryar
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review2016In: Procedia Computer Science / [ed] Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E., Elsevier, 2016, Vol. 100, p. 480-488Conference paper (Refereed)
    Abstract [en]

    OBJECTIVE: The objective of this paper is to investigate the goals and variables employed in the machine learning and microsimulation studies for the prognosis of dementia. METHOD: According to preset protocols, the Pubmed, Socups and Web of Science databases were searched to find studies that matched the defined inclusion/exclusion criteria, and then its references were checked for new studies. A quality checklist assessed the selected studies, and removed the low quality ones. The remaining ones (included set) had their data extracted and summarized. RESULTS: The summary of the data of the 37 included studies showed that the most common goal of the selected studies was the prediction of the conversion from mild cognitive impairment to Alzheimer's Disease, for studies that used machine learning, and cost estimation for the microsimulation ones. About the variables, neuroimaging was the most frequent used. CONCLUSIONS: The systematic literature review showed clear trends in prognosis of dementia research in what concerns machine learning techniques and microsimulation.

  • 22.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Machine learning applications in healthcare2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare.

    The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts.

    In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment.

    In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.

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  • 23.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Andersson, Ewa Kazimiera
    Linnaeus University.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Bohman, Doris
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Björling, Gunilla
    Jönköping University.
    Marcinowicz, Ludmiła
    Medical University of Bialystok, Poland.
    Stjernberg, Louise
    Malmö University.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Nursing Students’ Attitudes Toward Technology: Multicenter Cross-Sectional Study2024In: JMIR Medical Education, E-ISSN 2369-3762, Vol. 10, article id e50297Article in journal (Refereed)
    Abstract [en]

    Background: The growing presence of digital technologies in health care requires the health workforce to have proficiency in subjects such as informatics. This has implications in the education of nursing students, as their preparedness to use these technologies in clinical situations is something that course administrators need to consider. Thus, students’ attitudes toward technology could be investigated to assess their needs regarding this proficiency. Objective: This study aims to investigate attitudes (enthusiasm and anxiety) toward technology among nursing students and to identify factors associated with those attitudes. Methods: Nursing students at 2 universities in Sweden and 1 university in Poland were invited to answer a questionnaire. Data about attitudes (anxiety and enthusiasm) toward technology, eHealth literacy, electronic device skills, and frequency of using electronic devices and sociodemographic data were collected. Descriptive statistics were used to characterize the data. The Spearman rank correlation coefficient and Mann-Whitney U test were used for statistical inferences. Results: In total, 646 students answered the questionnaire—342 (52.9%) from the Swedish sites and 304 (47.1%) from the Polish site. It was observed that the students’ technology enthusiasm (techEnthusiasm) was on the higher end of the Technophilia instrument (score range 1-5): 3.83 (SD 0.90), 3.62 (SD 0.94), and 4.04 (SD 0.78) for the whole sample, Swedish students, and Polish students, respectively. Technology anxiety (techAnxiety) was on the midrange of the Technophilia instrument: 2.48 (SD 0.96), 2.37 (SD 1), and 2.60 (SD 0.89) for the whole sample, Swedish students, and Polish students, respectively. Regarding techEnthusiasm among the nursing students, a negative correlation with age was found for the Swedish sample (P<.001; ρSwedish=−0.201) who were generally older than the Polish sample, and positive correlations with the eHealth Literacy Scale score (P<.001; ρall=0.265; ρSwedish=0.190; ρPolish=0.352) and with the perceived skill in using computer devices (P<.001; ρall=0.360; ρSwedish=0.341; ρPolish=0.309) were found for the Swedish, Polish, and total samples. Regarding techAnxiety among the nursing students, a positive correlation with age was found in the Swedish sample (P<.001; ρSwedish=0.184), and negative correlations with eHealth Literacy Scale score (P<.001; ρall=−0.196; ρSwedish=−0.262; ρPolish=−0.133) and with the perceived skill in using computer devices (P<.001; ρall=−0.209; ρSwedish=−0.347; ρPolish=−0.134) were found for the Swedish, Polish, and total samples and with the semester only for the Swedish sample (P<.001; ρSwedish=−0.124). Gender differences were found regarding techAnxiety in the Swedish sample, with women exhibiting a higher mean score than men (2.451, SD 1.014 and 1.987, SD 0.854, respectively). Conclusions: This study highlights nursing students’ techEnthusiasm and techAnxiety, emphasizing correlations with various factors. With health care’s increasing reliance on technology, integrating health technology–related topics into education is crucial for future professionals to address health care challenges effectively. ©Ana Luiza Dallora, Ewa Kazimiera Andersson, Bruna Gregory Palm, Doris Bohman, Gunilla Björling, Ludmiła Marcinowicz, Louise Stjernberg, Peter Anderberg.

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  • 24.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Berner, Jessica
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Deriving learning outcomes for an applied health technology course for PhD students2024In: Journal of Teaching and Learning in Higher Education, E-ISSN 2004-4097, Vol. 5, no 1Article in journal (Other academic)
    Abstract [en]

    This study discusses the initial stage of development of a PhD course within the field of Applied Health Technology (AHT), in a multi-professional and transdisciplinary environment. The research aimed to align stakeholders' and PhD graduates' perspectives in order to create learning outcomes for a proposed AHT course. Semi-structured interviews were conducted with stakeholders and graduates of the programme, and the results were analysed using a qualitative content analysis method. The identified themes related to AHT perspectives, issues with working with AHT projects, programme goals, and course goals. These guided the creation of four strategically aligned learning outcomes for the proposed course.

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  • 25.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Eivazzadeh, Shahryar
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review2017In: PLOS ONE, E-ISSN 1932-6203, Vol. 12, no 6, article id e0179804Article in journal (Refereed)
    Abstract [en]

    Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases -Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies' different contexts. © 2017 Dallora et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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  • 26.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Kvist, Ola
    Karolinska University Hospital, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Ruiz, Sandra Diaz
    Karolinska University Hospital, SWE.
    Boldt, Martin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Flodmark, Carl-Erik
    Lund University, SWE.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach2020In: JMIR Medical Informatics, E-ISSN 2291-9694, Vol. 8, no 9, article id e18846Article in journal (Refereed)
    Abstract [en]

    Background: Bone age assessment (BAA) is used in numerous pediatric clinical settings, as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical since the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods suffer from drawbacks such as exposure of minors to radiation, do not consider factors that might affect the bone age and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA.

    Objective: This paper aims to investigate CA estimation through BAA in young individuals of 14 to 21 years with machine learning methods, addressing the drawbacks in the research using magnetic resonance imaging (MRI), assessment of multiple ROIs and other factors that may affect the bone age.

    Methods: MRI examinations of the radius, distal tibia, proximal tibia, distal femur and calcaneus were carried out on 465 males and 473 females subjects (14-21 years). Measures of weight and height were taken from the subjects and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, type of residence during upbringing). Two pediatric radiologists assessed, independently, the MRI images as to their stage of bone development (blinded to age, gender and each other). All the gathered information was used in training machine learning models for chronological age estimation and minor versus adults classification (threshold of 18 years). Different machine learning methods were investigated.

    Results: The minor versus adults classification produced accuracies of 90% and 84%, for male and female subjects, respectively, with high recalls for the classification of minors. The chronological age estimation for the eight age groups (14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter lower error occurred only for the ages of 14 and 15.

    Conclusions: This paper proposed to investigate the CA estimation through BAA using machine learning methods in two ways: minor versus adults classification and CA estimation in eight age groups (14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results, however, for the second case BAA showed not precise enough for the classification.

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  • 27.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Minku, Leandro
    University of Birmingham, GBR .
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Rennemark, Mikael
    Linnaeus University, SWE.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Multifactorial 10-year prior diagnosis prediction model of dementia2020In: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, Vol. 17, no 18, p. 1-18, article id 6674Article in journal (Refereed)
    Abstract [en]

    Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

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    Multifactorial 10-year prior diagnosis prediction model of dementia
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