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Can AI Help Reduce Disparities in General Medical and Mental Health Care?
- Source :
- AMA Journal of Ethics; Feb2019, Vol. 21 Issue 2, p167-179, 13p
- Publication Year :
- 2019
-
Abstract
- Background: As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve for all. Methods: Two case studies are examined using a machine learning algorithm on unstructured clinical and psychiatric notes to predict intensive care unit (ICU) mortality and 30-day psychiatric readmission with respect to race, gender, and insurance payer type as a proxy for socioeconomic status. Results: Clinical note topics and psychiatric note topics were heterogenous with respect to race, gender, and insurance payer type, which reflects known clinical findings. Differences in prediction accuracy and therefore machine bias are shown with respect to gender and insurance type for ICU mortality and with respect to insurance policy for psychiatric 30-day readmission. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
ARTIFICIAL intelligence
CONCEPTUAL structures
HEALTH services accessibility
HEALTH status indicators
HEALTH insurance
INTENSIVE care units
MACHINE learning
HEALTH policy
MENTAL health services
PROXY
RACE
RESEARCH evaluation
SEX distribution
SOCIOECONOMIC factors
PATIENT readmissions
HOSPITAL mortality
EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 23766980
- Volume :
- 21
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- AMA Journal of Ethics
- Publication Type :
- Academic Journal
- Accession number :
- 134591216
- Full Text :
- https://doi.org/10.1001/amajethics.2019.167