Machine Learning Enhances the Efficiency of Cognitive Screenings for Primary Care
| Autor: | Jacqueline Hogan, Allison Elber, Courtney W. Hess, Matthew Hogan, Boaz Levy, Sarah Greenspan, James M. Ellison, Kathryn Falcon, Daniel F. Driscoll, Ardeshir Z. Hashmi |
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| Rok vydání: | 2019 |
| Předmět: |
Male
Primary care Neuropsychological Tests Machine Learning 03 medical and health sciences 0302 clinical medicine medicine Humans Mass Screening Dementia Screening tool Cognitive impairment Aged 030304 developmental biology 0303 health sciences Primary Health Care Reproducibility of Results Cognition medicine.disease Psychiatry and Mental health Cognitive screening Female Neurology (clinical) Convergence (relationship) Geriatrics and Gerontology Cognition Disorders Psychology 030217 neurology & neurosurgery Cognitive psychology |
| Zdroj: | Journal of Geriatric Psychiatry and Neurology. 32:137-144 |
| ISSN: | 1552-5708 0891-9887 |
| Popis: | Background: Incorporation of cognitive screening into the busy primary care will require the development of highly efficient screening tools. We report the convergence validity of a very brief, self-administered, computerized assessment protocol against one of the most extensively used, clinician-administered instruments—the Montreal Cognitive Assessment (MoCA). Method: Two hundred six participants (mean age = 67.44, standard deviation [SD] = 11.63) completed the MoCA and the computerized test. Three machine learning algorithms (ie, Support Vector Machine, Random Forest, and Gradient Boosting Trees) were trained to classify participants according to the clinical cutoff score of the MoCA (ie, < 26) from participant performance on 25 features of the computerized test. Analysis employed Synthetic Minority Oversampling TEchnic to correct the sample for class imbalance. Results: Gradient Boosting Trees achieved the highest performance (accuracy = 0.81, specificity = 0.88, sensitivity = 0.74, F1 score = 0.79, and area under the curve = 0.81). A subsequent K-means clustering of the prediction features yielded 3 categories that corresponded to the unimpaired (mean = 26.98, SD = 2.35), mildly impaired (mean = 23.58, SD = 3.19), and moderately impaired (mean = 17.24, SD = 4.23) ranges of MoCA score ( F = 222.36, P < .00). In addition, compared to the MoCA, the computerized test correlated more strongly with age in unimpaired participants (ie, MoCA ≥26, n = 165), suggesting greater sensitivity to age-related changes in cognitive functioning. Conclusion: Future studies should examine ways to improve the sensitivity of the computerized test by expanding the cognitive domains it measures without compromising its efficiency. |
| Databáze: | OpenAIRE |
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