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Moraes, Ana Luiza DalloraORCID iD iconorcid.org/0000-0002-6752-017X
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Publications (10 of 23) Show all publications
Idrisoglu, A., Moraes, A. L., Anderberg, P. & Sanmartin Berglund, J. (2023). Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. Journal of Medical Internet Research, 25, Article ID e46105.
Open this publication in new window or tab >>Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review
2023 (English)In: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 25, article id e46105Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
JMIR Publications, 2023
Keywords
diagnosis, digital biomarkers, machine learning, monitoring, voice features, voice-affecting disorder, Humans, Monitoring, Physiologic, human, physiologic monitoring
National Category
Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:bth-25253 (URN)10.2196/46105 (DOI)001048954300007 ()2-s2.0-85165520794 (Scopus ID)
Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2024-02-20Bibliographically approved
Javeed, A., Saleem, M. A., Moraes, A. L., Ali, L., Sanmartin Berglund, J. & Anderberg, P. (2023). Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning. Applied Sciences, 13(8), Article ID 5188.
Open this publication in new window or tab >>Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 8, article id 5188Article in journal (Refereed) Published
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)).

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
heart morality, feature ranking, random forest, imbalance classes
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:bth-24631 (URN)10.3390/app13085188 (DOI)000980955700001 ()
Available from: 2023-05-26 Created: 2023-05-26 Last updated: 2023-05-26Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Idrisoglu, A., Ali, L., Rauf, H. T. & Anderberg, P. (2023). Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification. Biomedicines, 11(2), Article ID 439.
Open this publication in new window or tab >>Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
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2023 (English)In: Biomedicines, E-ISSN 2227-9059, Vol. 11, no 2, article id 439Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
dementia, feature fusion, machine learning, imbalance classes
National Category
Computer Sciences Geriatrics
Research subject
Applied Health Technology; Computer Science
Identifiers
urn:nbn:se:bth-24292 (URN)10.3390/biomedicines11020439 (DOI)000938259800001 ()2-s2.0-85148904251 (Scopus ID)
Projects
National E-Infrastructure for Aging Research (NEAR)
Available from: 2023-02-16 Created: 2023-02-16 Last updated: 2023-03-16Bibliographically approved
Berner, J., Moraes, A. L., Palm, B., Sanmartin Berglund, J. & Anderberg, P. (2023). Five-factor model, technology enthusiasm and technology anxiety. Digital Health, 9
Open this publication in new window or tab >>Five-factor model, technology enthusiasm and technology anxiety
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2023 (English)In: Digital Health, E-ISSN 2055-2076, Vol. 9Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
digital social participation, five-factor model, older adults, personality, Technology anxiety, technology enthusiasm
National Category
Gerontology, specialising in Medical and Health Sciences
Identifiers
urn:nbn:se:bth-25426 (URN)10.1177/20552076231203602 (DOI)001069602300001 ()2-s2.0-85171753514 (Scopus ID)
Available from: 2023-10-02 Created: 2023-10-02 Last updated: 2023-10-30Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Ali, A., Ali, L. & Anderberg, P. (2023). Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of medical systems, 47(1), Article ID 17.
Open this publication in new window or tab >>Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions
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2023 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 47, no 1, article id 17Article, review/survey (Refereed) Published
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).

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Deep learning, Dementia prediction, Feature selection, Machine learning, artificial intelligence, dementia, human, voice, Humans
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24274 (URN)10.1007/s10916-023-01906-7 (DOI)000920053700001 ()36720727 (PubMedID)2-s2.0-85147143895 (Scopus ID)
Note

Funding sponsor: NEAR - National E-Infrastructure for Aging Research

Available from: 2023-02-10 Created: 2023-02-10 Last updated: 2023-02-16Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Ali, A., Anderberg, P. & Ali, L. (2023). Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks. Computers, Materials and Continua, 75(2), 2491-2508
Open this publication in new window or tab >>Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks
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2023 (English)In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 75, no 2, p. 2491-2508Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Tech Science Press, 2023
Keywords
Dementia prediction, feature selection, genetic algorithm, neural networks, Deep neural networks, Diagnosis, Genetic algorithms, Learning systems, Neurodegenerative diseases, Cognitive decline, Detection/identification, Features selection, Neural-networks, Novel methods, Prediction modelling, Risk factors, Risk Identification, Societal impacts, Forecasting
National Category
Geriatrics Neurology
Identifiers
urn:nbn:se:bth-24482 (URN)10.32604/cmc.2023.033783 (DOI)000980836000009 ()2-s2.0-85152474169 (Scopus ID)
Projects
SNAC
Available from: 2023-05-01 Created: 2023-05-01 Last updated: 2023-05-26Bibliographically approved
Javeed, A., Sanmartin Berglund, J., Moraes, A. L., Saleem, M. A. & Anderberg, P. (2023). Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data. International Journal of Computational Intelligence Systems, 16(1), Article ID 188.
Open this publication in new window or tab >>Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data
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2023 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, no 1, article id 188Article in journal (Refereed) Published
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).

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023
Keywords
Computer vision, Deep learning, Feature engineering, Sleep apnea, Blood pressure, Brain, Cardiology, Diagnosis, Diseases, E-learning, Health risks, Heart, Learning systems, Sleep research, Electronic health, Feature engineerings, Health data, Machine learning models, Memory network, Obstructive sleep apnea, Predictive power, Risk factors, Long short-term memory
National Category
Computer Sciences Neurosciences
Identifiers
urn:nbn:se:bth-25692 (URN)10.1007/s44196-023-00362-y (DOI)001114808900001 ()2-s2.0-85177889861 (Scopus ID)
Projects
NEAR - National E-Infrastructure for Aging Research
Available from: 2023-12-08 Created: 2023-12-08 Last updated: 2023-12-31Bibliographically approved
Andersson, E. K., Moraes, A. L., Marcinowicz, L., Stjernberg, L., Björling, G., Anderberg, P. & Bohman, D. (2023). Self-Reported eHealth literacy among nursing students in Sweden and Poland: The eNursEd cross-sectional multicentre study. Health Informatics Journal, 29(4)
Open this publication in new window or tab >>Self-Reported eHealth literacy among nursing students in Sweden and Poland: The eNursEd cross-sectional multicentre study
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2023 (English)In: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 29, no 4Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
Cross-sectional multicentre study, eHealth literacy, nursing education, nursing student, Cross-Sectional Studies, Health Literacy, Humans, Poland, Self Report, Students, Nursing, Surveys and Questionnaires, Sweden, Telemedicine, clinical trial, cross-sectional study, human, multicenter study, questionnaire
National Category
Nursing Learning
Identifiers
urn:nbn:se:bth-25681 (URN)10.1177/14604582231214588 (DOI)001106545500001 ()2-s2.0-85177169125 (Scopus ID)
Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2023-12-08Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J. & Anderberg, P. (2022). An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning. Life, 12(7), Article ID 1097.
Open this publication in new window or tab >>An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning
2022 (English)In: Life, E-ISSN 2075-1729, Vol. 12, no 7, article id 1097Article in journal (Refereed) Published
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%.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
balanced accuracy, bachine learning, oversampling, dementia prediction
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-23527 (URN)10.3390/life12071097 (DOI)000831806600001 ()35888188 (PubMedID)
Note

open access

Available from: 2022-08-12 Created: 2022-08-12 Last updated: 2022-08-12Bibliographically approved
Ghazi, S. N., Anderberg, P., Sanmartin Berglund, J., Berner, J. & Moraes, A. L. (2022). Psychological Health and Digital Social Participation of the Older Adults during the COVID-19 Pandemic in Blekinge, Sweden—An Exploratory Study. International Journal of Environmental Research and Public Health, 19(6), Article ID 3711.
Open this publication in new window or tab >>Psychological Health and Digital Social Participation of the Older Adults during the COVID-19 Pandemic in Blekinge, Sweden—An Exploratory Study
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2022 (English)In: International Journal of Environmental Research and Public Health, E-ISSN 1660-4601, Vol. 19, no 6, article id 3711Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
gerontology; gerontechnology; aging; digital social participation; psychological health; COVID-19; public health; eHealth
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Applied Health Technology
Identifiers
urn:nbn:se:bth-22769 (URN)10.3390/ijerph19063711 (DOI)000775318900001 ()35329398 (PubMedID)2-s2.0-85126908041 (Scopus ID)
Projects
SNAC
Note

open access

SNAC is financially supported by the Ministry of Health and Social Affairs, Sweden, and the participating county councils, municipalities, and university departments. The authors are especially grateful to the participants of SNAC-Blekinge for their help and interest in this study.

Available from: 2022-03-23 Created: 2022-03-23 Last updated: 2022-04-19Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-6752-017X

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