Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning

no code implementations • 19 Mar 2024 • Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic, Jiang Bian, Yonghui Wu

The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives.

Transfer Learning

SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies

no code implementations • 6 Dec 2022 • 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

Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i. e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i. e., opioid use), and evaluate the extraction rate of SDoH using cancer populations.