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Updated: Jul 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Statistical modeling of clinical intake decisions
Abstract:
Modeled the intake decisions of four clinicians and a group of clinicians at a community mental health center by principal components-discriminant function analysis. The models of two clinicians and the group-based models withstood replication by the jackknife technique of discriminant analysis. Results showed that clinicians' written notes can be used to predict their decisions. We learned, for example, that the cues that are most important in arriving at intake decisions were therapy history, interview site, level of functioning, diagnosis, and behavioral disturbance.
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