Artificial Intelligence for Dementia Research
Methods Optimization.
ARXIV 2023:arXiv:2303.01949v1. [PMID:
36911275 PMCID:
PMC10002770]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
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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