DALL-M: Context-Aware Clinical Data Augmentation with LLMs

• URL: http://arxiv.org/abs/2407.08227v2
• Date: Mon, 7 Oct 2024 09:51:46 GMT
• Title: DALL-M: Context-Aware Clinical Data Augmentation with LLMs
• Authors: Chihcheng Hsieh, Catarina Moreira, Isabel Blanco Nobre, Sandra Costa Sousa, Chun Ouyang, Margot Brereton, Joaquim Jorge, Jacinto C. Nascimento,
• Abstract summary: Radiologists often find chest X-rays insufficient for diagnosing underlying diseases. We present a novel framework to enhance the clinical context through augmentation techniques with clinical data. We introduce a pioneering approach to clinical data augmentation that employs large language models to generate patient contextual synthetic data.
• Score: 13.827368628263997
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating comprehensive clinical features and data integration. We present a novel framework to enhance the clinical context through augmentation techniques with clinical tabular data, thereby improving its applicability and reliability in AI medical diagnostics. We introduce a pioneering approach to clinical data augmentation that employs large language models to generate patient contextual synthetic data. This methodology is crucial for training more robust deep learning models in healthcare. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance. Our methodology, termed DALL-M, uses a three-phase feature generation process: (i)clinical context storage, (ii)expert query generation, and (iii)context-aware feature augmentation. DALL-M generates new, clinically relevant features by synthesizing chest X-ray images and reports. Applied to 799 cases using nine features from the MIMIC-IV dataset, it created an augmented set of 91 features. This is the first work to generate contextual values for patients' X-ray reports. Specifically, we provide (i)the capacity of LLMs to generate contextual synthetic values for existing clinical features and (ii)their ability to create entirely new clinically relevant features. Empirical validation with machine learning models showed significant performance improvements. Incorporating augmented features increased the F1 score by 16.5% and Precision and Recall by approximately 25%. DALL-M addresses a critical gap in clinical data augmentation, offering a robust framework for generating contextually enriched datasets.

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