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Artificial Intelligence for Dementia Research Methods Optimization

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research...

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Autores principales: Bucholc, Magda, James, Charlotte, Al Khleifat, Ahmad, Badhwar, AmanPreet, Clarke, Natasha, Dehsarvi, Amir, Madan, Christopher R., Marzi, Sarah J., Shand, Cameron, Schilder, Brian M., Tamburin, Stefano, Tantiangco, Hanz M., Lourida, Ilianna, Llewellyn, David J., Ranson, Janice M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002770/
https://www.ncbi.nlm.nih.gov/pubmed/36911275
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author Bucholc, Magda
James, Charlotte
Al Khleifat, Ahmad
Badhwar, AmanPreet
Clarke, Natasha
Dehsarvi, Amir
Madan, Christopher R.
Marzi, Sarah J.
Shand, Cameron
Schilder, Brian M.
Tamburin, Stefano
Tantiangco, Hanz M.
Lourida, Ilianna
Llewellyn, David J.
Ranson, Janice M.
author_facet Bucholc, Magda
James, Charlotte
Al Khleifat, Ahmad
Badhwar, AmanPreet
Clarke, Natasha
Dehsarvi, Amir
Madan, Christopher R.
Marzi, Sarah J.
Shand, Cameron
Schilder, Brian M.
Tamburin, Stefano
Tantiangco, Hanz M.
Lourida, Ilianna
Llewellyn, David J.
Ranson, Janice M.
author_sort Bucholc, Magda
collection PubMed
description INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.
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spelling pubmed-100027702023-03-11 Artificial Intelligence for Dementia Research Methods Optimization Bucholc, Magda James, Charlotte Al Khleifat, Ahmad Badhwar, AmanPreet Clarke, Natasha Dehsarvi, Amir Madan, Christopher R. Marzi, Sarah J. Shand, Cameron Schilder, Brian M. Tamburin, Stefano Tantiangco, Hanz M. Lourida, Ilianna Llewellyn, David J. Ranson, Janice M. ArXiv Article INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia. Cornell University 2023-03-02 /pmc/articles/PMC10002770/ /pubmed/36911275 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Bucholc, Magda
James, Charlotte
Al Khleifat, Ahmad
Badhwar, AmanPreet
Clarke, Natasha
Dehsarvi, Amir
Madan, Christopher R.
Marzi, Sarah J.
Shand, Cameron
Schilder, Brian M.
Tamburin, Stefano
Tantiangco, Hanz M.
Lourida, Ilianna
Llewellyn, David J.
Ranson, Janice M.
Artificial Intelligence for Dementia Research Methods Optimization
title Artificial Intelligence for Dementia Research Methods Optimization
title_full Artificial Intelligence for Dementia Research Methods Optimization
title_fullStr Artificial Intelligence for Dementia Research Methods Optimization
title_full_unstemmed Artificial Intelligence for Dementia Research Methods Optimization
title_short Artificial Intelligence for Dementia Research Methods Optimization
title_sort artificial intelligence for dementia research methods optimization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002770/
https://www.ncbi.nlm.nih.gov/pubmed/36911275
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