Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.
Alzheimers Res Ther
; 16(1): 175, 2024 Aug 01.
Article
em En
| MEDLINE
| ID: mdl-39085973
ABSTRACT
Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Demência
/
Aprendizado de Máquina
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article