Extracting Patient History from Clinical Text: A Comparative Study of Clinical Large Language Models

Extracting medical history entities (MHEs) related to a patient's chief complaint (CC), history of present illness (HPI), and past, family, and social history (PFSH) helps structure free-text clinical notes into standardized EHRs, streamlining downstream tasks like continuity of care, medical coding, and quality metrics. Fine-tuned clinical large language models (cLLMs) can assist in this process while ensuring the protection of sensitive data via on-premises deployment. This study evaluates the performance of cLLMs in recognizing CC/HPI/PFSH-related MHEs and examines how note characteristics impact model accuracy. We annotated 1,449 MHEs across 61 outpatient-related clinical notes from the MTSamples repository. To recognize these entities, we fine-tuned seven state-of-the-art cLLMs. Additionally, we assessed the models' performance when enhanced by integrating, problems, tests, treatments, and other basic medical entities (BMEs). We compared the performance of these models against GPT-4o in a zero-shot setting. To further understand the textual characteristics affecting model accuracy, we conducted an error analysis focused on note length, entity length, and segmentation. The cLLMs showed potential in reducing the time required for extracting MHEs by over 20%. However, detecting many types of MHEs remained challenging due to their polysemous nature and the frequent involvement of non-medical vocabulary. Fine-tuned GatorTron and GatorTronS, two of the most extensively trained cLLMs, demonstrated the highest performance. Integrating pre-identified BME information improved model performance for certain entities. Regarding the impact of textual characteristics on model performance, we found that longer entities were harder to identify, note length did not correlate with a higher error rate, and well-organized segments with headings are beneficial for the extraction.

Low-resource classification of mobility functioning information in clinical sentences using large language models

Objective: Function is increasingly recognized as an important indicator of whole-person health. This study evaluates the ability of publicly available large language models (LLMs) to accurately identify the presence of functioning information from clinical notes. We explore various strategies to improve the performance on this task. Materials and Methods: We collect a balanced binary classification dataset of 1000 sentences from the Mobility NER dataset, which was curated from n2c2 clinical notes. For evaluation, we construct zero-shot and few-shot prompts to query the LLMs whether a given sentence contains mobility functioning information. Two sampling techniques, random sampling and k-nearest neighbor (kNN)-based sampling, are used to select the few-shot examples. Furthermore, we apply a parameter-efficient prompt-based fine-tuning method to the LLMs and evaluate their performance under various training settings. Results: Flan-T5-xxl outperforms all other models in both zero-shot and few-shot settings, achieving a F1 score of 0.865 with a single demonstrative example selected by kNN sampling. In prompt-based fine-tuning experiments, this foundation model also demonstrates superior performance across all low-resource settings, particularly achieving an impressive F1 score of 0.922 using the full training dataset. The smaller model, Flan-T5-xl, requires fine-tuning with only 2.3M additional parameters to achieve comparable performance to the fully fine-tuned Gatortron-base model, both surpassing 0.9 F1 score. Conclusion: Open-source instruction-tuned LLMs demonstrate impressive in-context learning capability in the mobility functioning classification task. The performance of these models can be further improved by continuing fine-tuning on a task-specific dataset.

Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes

Function is increasingly recognized as an important indicator of whole-person health, although it receives little attention in clinical natural language processing research. We introduce the first public annotated dataset specifically on the Mobility domain of the International Classification of Functioning, Disability and Health (ICF), aiming to facilitate automatic extraction and analysis of functioning information from free-text clinical notes. We utilize the National NLP Clinical Challenges (n2c2) research dataset to construct a pool of candidate sentences using keyword expansion. Our active learning approach, using query-by-committee sampling weighted by density representativeness, selects informative sentences for human annotation. We train BERT and CRF models, and use predictions from these models to guide the selection of new sentences for subsequent annotation iterations. Our final dataset consists of 4,265 sentences with a total of 11,784 entities, including 5,511 Action entities, 5,328 Mobility entities, 306 Assistance entities, and 639 Quantification entities. The inter-annotator agreement (IAA), averaged over all entity types, is 0.72 for exact matching and 0.91 for partial matching. We also train and evaluate common BERT models and state-of-the-art Nested NER models. The best F1 scores are 0.84 for Action, 0.7 for Mobility, 0.62 for Assistance, and 0.71 for Quantification. Empirical results demonstrate promising potential of NER models to accurately extract mobility functioning information from clinical text. The public availability of our annotated dataset will facilitate further research to comprehensively capture functioning information in electronic health records (EHRs).