Use of machine learning in geriatric clinical care for chronic diseases: A systematic literature review

Avishek Choudhury, Emily Renjilian, Onur Asan

Research output: Contribution to journalReview articlepeer-review


Objectives: Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods: We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results: We identified 35 eligible studies and classified in three groups: psychological disorder (n ¼ 22), eye diseases (n ¼ 6), and others (n ¼ 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion: More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.

Original languageEnglish
Pages (from-to)459-471
Number of pages13
JournalJAMIA Open
Issue number3
StatePublished - 2020

ASJC Scopus subject areas

  • Health Informatics


  • AI standards
  • Artificial intelligence
  • Chronic diseases
  • Comorbidity
  • Data governance
  • Geriatric
  • Machine learning
  • Multimorbidity
  • Older patients

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