A dataset and benchmark for hospital course summarization with adapted large language models
Stanford University（斯坦福大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash S. Teh… 展开作者
Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash S. Tehrani, Jangwon Kim, Akshay S. Chaudhari
Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel pre-processed dataset, the MIMIC-IV-BHC, encapsulating clinical note and brief hospital course (BHC) pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization performance of two general-purpose LLMs and three healthcare-adapted LLMs. Using clinical notes as input, we apply prompting-based (using in-context learning) and fine-tuning-based adaptation strategies to three open-source LLMs (Clinical-T5-Large, Llama2-13B, FLAN-UL2) and two proprietary LLMs (GPT-3.5, GPT-4). We evaluate these LLMs across multiple context-length inputs using natural language similarity metrics. We further conduct a clinical study with five clinicians, comparing clinician-written and LLM-generated BHCs across 30 samples, focusing on their potential to enhance clinical decision-making through improved summary quality. We observe that the Llama2-13B fine-tuned LLM outperforms other domain-adapted models given quantitative evaluation metrics of BLEU and BERT-Score. GPT-4 with in-context learning shows more robustness to increasing context lengths of clinical note inputs than fine-tuned Llama2-13B. Despite comparable quantitative metrics, the reader study depicts a significant preference for summaries generated by GPT-4 with in-context learning compared to both Llama2-13B fine-tuned summaries and the original summaries, highlighting the need for qualitative clinical evaluation.
Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；School of Medicine and Public Health（医学与公共卫生学院）；Loyola University Chicago（芝加哥洛约拉大学）；Harvard Medical School（哈佛医学院）
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Hyderabad Campus（计算机科学与信息系统系，比拉理工学院，比拉理工学院，海得拉巴校区）
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院NHS基金会信托）；University College London（伦敦大学学院）
A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews
Department of Computer Science & Information Systems, Birla Institute of Technology and Science, Pilani, India（印度比拉理工学院计算机科学与信息系统系）；Birdeye Inc.（Birdeye公司）
Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study
University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学园区）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Aberdeen（阿伯丁大学）；Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Loyola University Chicago（芝加哥洛约拉大学）
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
University of Applied Sciences and Arts Dortmund（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）；Bielefeld University of Applied Sciences and Arts（比勒费尔德应用科学与艺术大学）
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes
University of Wisconsin（威斯康星大学）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
