Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. (February 2020)

• Record Type: Journal Article
• Authors: Graham, Sarah A.; Lee, Ellen E.; Jeste, Dilip V.; Van Patten, Ryan; Twamley, Elizabeth W.; Nebeker, Camille; Yamada, Yasunori; Kim, Ho-Cheol; Depp, Colin A.
• Is Part Of: Psychiatry research. Volume 284(2020)
• Publication Date: 2020-02
• Subjects: Dementia -- Mild cognitive impairment -- Machine learning -- Sensors -- Natural language processing; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2019.112732
• Languages: English
• ISSNs: 0165-1781

Abstract

Highlights: Artificial Intelligence has great potential to advance diagnosis and treatment of patients with neurocognitive disorders Multi-feature datasets can improve personalization and predictive ability of machine learning algorithms in healthcare. Development of Explainable Artificial Intelligence is warranted to establish trust in models for clinical decision-making. Engagement of clinicians and establishment of ethical guidelines for Artificial Intelligence use in healthcare is necessary. Artificial Intelligence models must be developed with "human in the loop" focus to enable better clinical decision-making. Abstract: Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.