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Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-6752-017X
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0002-0316-548x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0449-5322
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0003-4312-2246
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2017 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 12, no 6, article id e0179804Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Public Library of Science , 2017. Vol. 12, no 6, article id e0179804
Keywords [en]
Alzheimer disease, analytic method, artificial neural network, Bayesian Network, classification algorithm, comorbidity, DecisionTrees, disease course, human, k nearest neighbor, machine learning, microsimulation technique, mild cognitive impairment, population research, quality control, Review, support vector machine, systematic review
National Category
Geriatrics Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15017DOI: 10.1371/journal.pone.0179804ISI: 000404608300049Scopus ID: 2-s2.0-85021683292OAI: oai:DiVA.org:bth-15017DiVA, id: diva2:1135391
Note

Open access

Available from: 2017-08-23 Created: 2017-08-23 Last updated: 2023-12-04Bibliographically approved
In thesis
1. Machine learning applications in healthcare
Open this publication in new window or tab >>Machine learning applications in healthcare
2020 (English)Doctoral 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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 202
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 6
Keywords
Machine learning, Healthcare, Diagnosis, Prognosis, Age assessment, Bone age assessment, Dementia, Applied health technology
National Category
Other Medical Sciences
Research subject
Applied Health Technology
Identifiers
urn:nbn:se:bth-19513 (URN)978-91-7295-405-2 (ISBN)
Public defence
2020-09-16, 14:00
Opponent
Supervisors
Available from: 2020-05-29 Created: 2020-05-28 Last updated: 2021-01-27Bibliographically approved

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Moraes, Ana Luiza DalloraEivazzadeh, ShahryarMendes, EmiliaBerglund, JohanAnderberg, Peter

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