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Objective Alzheimer disease (AD) is the most common cause of dementia, asyndrome characterized by cognitive impairment severe enough to interfere withactivities of daily life. We aimed to conduct a systematic literature review(SLR) of studies that applied machine learning (ML) methods to clinical dataderived from electronic health records in order to model risk for progressionof AD dementia.  Materials and Methods: We searched for articles published between January 1,2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore DigitalLibrary, Association for Computing Machinery Digital Library, and arXiv. Weused predefined criteria to select relevant articles and summarized themaccording to key components of ML analysis such as data characteristics,computational algorithms, and research focus.  Results: There has been a considerable rise over the past 5 years in thenumber of research papers using ML-based analysis for AD dementia modeling. Wereviewed 64 relevant articles in our SLR. The results suggest that majority ofexisting research has focused on predicting progression of AD dementia usingpublicly available datasets containing both neuroimaging and clinical data(neurobehavioral status exam scores, patient demographics, neuroimaging data,and laboratory test values).  Discussion: Identifying individuals at risk for progression of AD dementiacould potentially help to personalize disease management to plan future care.Clinical data consisting of both structured data tables and clinical notes canbe effectively used in ML-based approaches to model risk for AD dementiaprogression. Data sharing and reproducibility of results can enhance theimpact, adaptation, and generalizability of this research.

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review