Social Determinant
Social determinants of health (SDOH) encompass the environmental and societal factors influencing health outcomes, and research focuses on accurately identifying and incorporating these factors into healthcare models and decision-making. Current studies leverage large language models (LLMs) and machine learning (ML) algorithms, including transformer-based architectures and gradient boosting machines, to extract SDOH information from unstructured clinical notes and other data sources, aiming to improve prediction accuracy and address algorithmic bias. This research is significant because it promises to improve healthcare equity by identifying and mitigating disparities in health outcomes linked to SDOH, leading to more effective interventions and personalized care.
Papers
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Zehao Yu, Xi Yang, Chong Dang, Prakash Adekkanattu, Braja Gopal Patra, Yifan Peng, Jyotishman Pathak, Debbie L. Wilson, Ching-Yuan Chang, Wei-Hsuan Lo-Ciganic, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu
Automated Identification of Eviction Status from Electronic Health Record Notes
Zonghai Yao, Jack Tsai, Weisong Liu, David A. Levy, Emily Druhl, Joel I Reisman, Hong Yu