Comparing the influence of social risk factors on machine learning model performance across racial and ethnic groups in home healthcare.

Mollie Hobensack¹, Anahita Davoudi², Jiyoun Song³, Kenrick Cato⁴, Kathryn H Bowles⁵, Maxim Topaz⁶

¹Icahn School of Medicine at Mount Sinai, New York, NY. Electronic address: mollie.hobensack@mssm.edu.

²Center for Home Care Policy & Research, VNS Health, New York, NY.

³University of Pennsylvania School of Nursing, Philadelphia, PA.

⁴University of Pennsylvania School of Nursing, Philadelphia, PA; Children's Hospital of Philadelphia, Philadelphia, PA.

⁵Center for Home Care Policy & Research, VNS Health, New York, NY; University of Pennsylvania School of Nursing, Philadelphia, PA.

⁶Center for Home Care Policy & Research, VNS Health, New York, NY; Columbia University School of Nursing, New York, NY; Data Science Institute, Columbia University, New York, NY.

PubMed PMC DOI PDF

Abstract

This study examined the impact of social risk factors on machine learning model performance for predicting hospitalization and emergency department visits in home healthcare. Using retrospective data from one U.S. home healthcare agency, four models were developed with unstructured social information documented in clinical notes. Performance was compared with and without social factors. A subgroup…

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