  Objective Alzheimer disease (AD) is the most common cause of dementia, a
syndrome characterized by cognitive impairment severe enough to interfere with
activities of daily life. We aimed to conduct a systematic literature review
(SLR) of studies that applied machine learning (ML) methods to clinical data
derived from electronic health records in order to model risk for progression
of AD dementia.
  Materials and Methods: We searched for articles published between January 1,
2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital
Library, Association for Computing Machinery Digital Library, and arXiv. We
used predefined criteria to select relevant articles and summarized them
according 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 the
number of research papers using ML-based analysis for AD dementia modeling. We
reviewed 64 relevant articles in our SLR. The results suggest that majority of
existing research has focused on predicting progression of AD dementia using
publicly 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 dementia
could potentially help to personalize disease management to plan future care.
Clinical data consisting of both structured data tables and clinical notes can
be effectively used in ML-based approaches to model risk for AD dementia
progression. Data sharing and reproducibility of results can enhance the
impact, adaptation, and generalizability of this research.
