Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data
CoRR(2024)
摘要
The inherent complexity of structured longitudinal Electronic Health Records
(EHR) data poses a significant challenge when integrated with Large Language
Models (LLMs), which are traditionally tailored for natural language
processing. Motivated by the urgent need for swift decision-making during new
disease outbreaks, where traditional predictive models often fail due to a lack
of historical data, this research investigates the adaptability of LLMs, like
GPT-4, to EHR data. We particularly focus on their zero-shot capabilities,
which enable them to make predictions in scenarios in which they haven't been
explicitly trained. In response to the longitudinal, sparse, and
knowledge-infused nature of EHR data, our prompting approach involves taking
into account specific EHR characteristics such as units and reference ranges,
and employing an in-context learning strategy that aligns with clinical
contexts. Our comprehensive experiments on the MIMIC-IV and TJH datasets
demonstrate that with our elaborately designed prompting framework, LLMs can
improve prediction performance in key tasks such as mortality, length-of-stay,
and 30-day readmission by about 35%, surpassing ML models in few-shot
settings. Our research underscores the potential of LLMs in enhancing clinical
decision-making, especially in urgent healthcare situations like the outbreak
of emerging diseases with no labeled data. The code is publicly available at
https://github.com/yhzhu99/llm4healthcare for reproducibility.
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