Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
This addresses information overload for healthcare providers by automating problem list summarization from clinical notes, though it is incremental as it applies existing models to a new medical task.
The paper tackled the problem of automatically summarizing patients' main problems from hospital progress notes by proposing a new NLP task and evaluating T5 and BART models, with T5 achieving significant performance gains over rule-based systems and general domain models as measured by metrics like ROUGE and F-score.
Automatically summarizing patients' main problems from daily progress notes using natural language processing methods helps to battle against information and cognitive overload in hospital settings and potentially assists providers with computerized diagnostic decision support. Problem list summarization requires a model to understand, abstract, and generate clinical documentation. In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization. We investigate the performance of T5 and BART, two state-of-the-art seq2seq transformer architectures, in solving this problem. We provide a corpus built on top of progress notes from publicly available electronic health record progress notes in the Medical Information Mart for Intensive Care (MIMIC)-III. T5 and BART are trained on general domain text, and we experiment with a data augmentation method and a domain adaptation pre-training method to increase exposure to medical vocabulary and knowledge. Evaluation methods include ROUGE, BERTScore, cosine similarity on sentence embedding, and F-score on medical concepts. Results show that T5 with domain adaptive pre-training achieves significant performance gains compared to a rule-based system and general domain pre-trained language models, indicating a promising direction for tackling the problem summarization task.