2021/11/18 by Songzi Liu, Yuan Luo, Liu, Songzi +1 · 1 citation
Computer Science · Environmental Science · Health Professions · Medicine · Psychology · Social Sciences · #Climate Change and Health Impacts #Computer science #Diversity (politics) #Economic growth #Economics #Environmental health #Ethnic group #Health care #Healthcare cost, quality, practices #Insurance, Mortality, Demography, Risk Management #Machine learning #Medicine #Political science #Population #Psychology #Subgroup analysis #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.09507
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/11/18 · openalex publication_date 2021/11/18 · arxiv updated 2021/11/19 · openalex created_date 2021/11/22 · openalex updated_date 2026/08/08
Machine learning in medicine leverages the wealth of healthcare data to extract knowledge, facilitate clinical decision-making, and ultimately improve care delivery. However, ML models trained on datasets that lack demographic diversity could yield suboptimal performance when applied to the underrepresented populations (e.g. ethnic minorities, lower social-economic status), thus perpetuating health disparity. In this study, we evaluated four classifiers built to predict Hyperchloremia - a condition that often results from aggressive fluids administration in the ICU population - and compared their performance in racial, gender, and insurance subgroups. We observed that adding social determinants features in addition to the lab-based ones improved model performance on all patients. The subgroup testing yielded significantly different AUC scores in 40 out of the 44 model-subgroup, suggesting disparities when applying ML models to social determinants subgroups. We urge future researchers to design models that proactively adjust for potential biases and include subgroup reporting in their studies.