Automated Clinical Data Extraction with Knowledge Conditioned LLMs

• URL: http://arxiv.org/abs/2406.18027v1
• Date: Wed, 26 Jun 2024 02:49:28 GMT
• Title: Automated Clinical Data Extraction with Knowledge Conditioned LLMs
• Authors: Diya Li, Asim Kadav, Aijing Gao, Rui Li, Richard Bourgon,
• Abstract summary: Large language models (LLMs) can be effective at interpreting unstructured text in reports, but they often hallucinate due to a lack of domain-specific knowledge. We propose a novel framework that aligns generated internal knowledge with external knowledge through in-context learning (ICL) Our framework employs a retriever to identify relevant units of internal or external knowledge and a grader to evaluate the truthfulness and helpfulness of the retrieved internal-knowledge rules.
• Score: 7.935125803100394
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The extraction of lung lesion information from clinical and medical imaging reports is crucial for research on and clinical care of lung-related diseases. Large language models (LLMs) can be effective at interpreting unstructured text in reports, but they often hallucinate due to a lack of domain-specific knowledge, leading to reduced accuracy and posing challenges for use in clinical settings. To address this, we propose a novel framework that aligns generated internal knowledge with external knowledge through in-context learning (ICL). Our framework employs a retriever to identify relevant units of internal or external knowledge and a grader to evaluate the truthfulness and helpfulness of the retrieved internal-knowledge rules, to align and update the knowledge bases. Our knowledge-conditioned approach also improves the accuracy and reliability of LLM outputs by addressing the extraction task in two stages: (i) lung lesion finding detection and primary structured field parsing, followed by (ii) further parsing of lesion description text into additional structured fields. Experiments with expert-curated test datasets demonstrate that this ICL approach can increase the F1 score for key fields (lesion size, margin and solidity) by an average of 12.9% over existing ICL methods.

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