Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching (2303.16756v2)

Abstract: The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of LLMs to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles.

• The paper introduces LLM-PTM, a method that leverages LLMs for generating privacy-aware, semantically enriched trial criteria to enhance patient-trial matching.
• The approach utilizes Chain-of-Thought prompting and BERT embeddings to preserve semantic consistency, resulting in up to a 12% boost in model generalizability.
• Experimental results show a 7.32% improvement in precision, recall, and F1 scores, underscoring its effectiveness across diverse clinical trials.

LLMs for Healthcare Data Augmentation: An Example on Patient-Trial Matching

This essay provides a comprehensive summary of the paper "LLMs for Healthcare Data Augmentation: An Example on Patient-Trial Matching". The paper introduces a method utilizing LLMs to enhance patient-trial matching processes, addressing existing challenges in healthcare data standardization and privacy preservation. The methodology, experimental results, and implications for future research are explored in depth.

Introduction

Patient-trial matching is pivotal in clinical research, targeting the alignment of patient profiles with trial criteria for optimal recruitment. Traditional approaches face interoperability issues between Electronic Health Records (EHRs) and clinical trial descriptions due to differing ontologies and terminologies. LLMs offer a solution by leveraging sophisticated natural language processing capabilities to harmonize these discrepancies. This paper explores a privacy-aware data augmentation technique termed LLM-PTM, which focuses on generating semantically consistent data while safeguarding patient privacy. The method demonstrates enhancements in both accuracy and generalizability across multiple trials, highlighting the potential application of LLMs in clinical trial recruitment.

Methodology

The paper delineates a novel approach for patient-trial matching using augmented data generated by LLMs. The primary challenge is preserving sensitive patient data privacy during augmentation. To address this, the method desensitizes patient data before using it for augmentation.

Trial Eligibility Criteria Augmentation

The augmentation process involves the use of LLMs to generate additional trial criteria while maintaining semantic integrity. By employing Chain-of-Thought prompting, the LLMs are directed to generate prompts that transform trial criteria into a machine-readable format without altering meaning. The augmented criteria help train models more effectively, as depicted in the illustrative framework (Figure 1).

Figure 1: Illustration of LLM-PTM augmented criteria.

Patient and Criteria Embedding

Latent representations of patient records and trial criteria are achieved using pretrained BERT embeddings. The memory networks preserve sequence data in embedding space, crucial for capturing semantic relationships between EHR components and trial criteria. This process enhances the model's capability to align patient records with trial eligibility criteria effectively.

Prediction and Embedding Learning

A composite loss function consisting of classification and contrastive loss terms is designed to maximize matching accuracy. The model simultaneously optimizes patient-trial matching and distinguishes between inclusion and exclusion criteria by evaluating embedding similarities. The comprehensive framework is detailed in Figure 2.

Figure 2: Overall model framework.

Experimental Results

Overall Performance

The experiments demonstrate a 7.32% improvement in precision, recall, and F1 scores over baseline models, indicating enhanced model capability with augmented data. The approach significantly improves model generalization, marking a 12.12% increase when applied to new trials.

Performance Across Trials

The paper reveals varying performance levels across different trials with LLM-PTM consistently outperforming baseline models. In particular, Trials 1 and 6 see the most significant performance boost, attributed to the augmented model's ability to handle complex datasets more efficiently.

Generalizability

LLM-PTM's capability to generalize across varied trials is evaluated through designated scenarios. The model shows robust performance in transposed-test cases, outperforming baseline results by substantial margins, demonstrating its adaptability in different trial contexts.

Case Study

The case paper illustrates the efficacy of LLM-PTM through two examples: hard data easing and semantic enrichment. The method effectively mitigates challenges associated with complex data and enhances the semantic richness of datasets, further supporting model accuracy.

Conclusion

This paper demonstrates the potential of employing LLMs for data augmentation in patient-trial matching within the healthcare sector. By addressing key issues related to data privacy and semantic interoperability, LLM-PTM significantly enhances model performance and generalizability. The approach sets the stage for further exploration of LLM-based solutions in other healthcare domains, emphasizing the critical impact of enhanced data augmentation in improving clinical trial outcomes. Future studies will likely expand on these findings, applying LLM-PTM to broader datasets, further bridging the gap between EHRs and clinical trial descriptions.