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Demographics, Social determinants of health, and family history documented inthe unstructured text within the electronic health records are increasinglybeing studied to understand how this information can be utilized with thestructured data to improve healthcare outcomes. After the GPT models werereleased, many studies have applied GPT models to extract this information fromthe narrative clinical notes. Different from the existing work, our researchfocuses on investigating the zero-shot learning on extracting this informationtogether by providing minimum information to the GPT model. We utilizede-identified real-world clinical notes annotated for demographics, varioussocial determinants, and family history information. Given that the GPT modelmight provide text different from the text in the original data, we explore twosets of evaluation metrics, including the traditional NER evaluation metricsand semantic similarity evaluation metrics, to completely understand theperformance. Our results show that the GPT-3.5 method achieved an average of0.975 F1 on demographics extraction, 0.615 F1 on social determinantsextraction, and 0.722 F1 on family history extraction. We believe these resultscan be further improved through model fine-tuning or few-shots learning.Through the case studies, we also identified the limitations of the GPT models,which need to be addressed in future research.

Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model