Fine-tuning large language models for effective nutrition support in residential aged care: a domain expertise approach

Abstract

Purpose: Malnutrition is a serious health concern, particularly among the older people living in residential aged care facilities. An automated and efficient method is required to identify the individuals afflicted with malnutrition in this setting. The recent advancements in transformer-based large language models (LLMs) equipped with sophisticated context-aware embeddings, such as RoBERTa, have significantly improved machine learning performance, particularly in predictive modelling. Enhancing the embeddings of these models on domain-specific corpora, such as clinical notes, is essential for elevating their performance in clinical tasks. Therefore, our study introduces a novel approach that trains a foundational RoBERTa model on nursing progress notes to develop a RAC domain-specific LLM. The model is further fine-tuned on nursing progress notes to enhance malnutrition identification and prediction in residential aged care setting. Methods: We develop our domain-specific model by training the RoBERTa LLM on 500,000 nursing progress notes from residential aged care electronic health records (EHRs). The model embeddings were used for two downstream tasks: malnutrition note identification and malnutrition prediction. Its performance was compared against baseline RoBERTa and BioClinicalBERT. Furthermore, we truncated long sequence text to fit into RoBERTa 512-token sequence length limitation, enabling our model to handle sequences up to1536 tokens. Results: Utilizing 5-fold cross-validation for both tasks, our RAC domain-specific LLM demonstrated significantly better performance over other models. In malnutrition note identification, it achieved a slightly higher F1-score of 0.966 compared to other LLMs. In prediction, it achieved significantly higher F1-score of 0.655. We enhanced our model predictive capability by integrating the risk factors extracted from each client notes, creating a combined data layer of structured risk factors and free-text notes. This integration improved the prediction performance, evidenced by an increased F1-score of 0.687. Conclusion: Our findings suggest that further fine-tuning a large language model on a domain-specific clinical corpus can improve the foundational model performance in clinical tasks. This specialized adaptation significantly improves our domain-specific model performance in tasks such as malnutrition risk identification and malnutrition prediction, making it useful for identifying and predicting malnutrition among older people living in residential aged care or long-term care facilities.