ArticleProceedings of COLING. International Conference on Computational Linguistics2022
Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models.
Article in Proceedings of COLING. International Conference on Computational Linguistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- A scoping review of natural language processing in addressing medically inaccurate information: Errors, misinformation, and hallucination.Journal of biomedical informatics · 2025Article
- Evaluating the effectiveness of biomedical fine-tuning for large language models on clinical tasks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Scientific Evidence for Clinical Text Summarization Using Large Language Models: Scoping Review.Journal of medical Internet research · 2025Article
- Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study.JMIR AI · 2025Article
- Current and future state of evaluation of large language models for medical summarization tasks.npj health systems · 2025Article
- On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.Journal of biomedical informatics · 2024Article
- Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study.JMIR mental health · 2024Article
- Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls.Applied clinical informatics · 2024Article
- Potential of Large Language Models in Health Care: Delphi Study.Journal of medical Internet research · 2024Article
- The Future Role of Generative Artificial Intelligence (AI) in Medicine.Journal of clinical practice and research · 2024Article
- Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.medRxiv : the preprint server for health sciences · 2024Article
- Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks.Journal of medical systems · 2024Review
- Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden.medRxiv : the preprint server for health sciences · 2023Article
- A method to automate the discharge summary hospital course for neurology patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
Identifiers
36268128PMC9581107What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.