Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

• URL: http://arxiv.org/abs/2005.01795v3
• Date: Wed, 2 Jun 2021 14:48:09 GMT
• Title: Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
• Authors: Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham, Zachary C. Lipton
• Abstract summary: We introduce the first complete pipelines to leverage deep summarization models to generate SOAP notes. We propose Cluster2Sent, an algorithm that extracts important utterances relevant to each summary section. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.
• Score: 43.13248746968624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.

Related papers

• Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
  We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback. Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
  arXiv  Detail & Related papers  (2024-07-05T20:25:04Z)
• Hierarchical Indexing for Retrieval-Augmented Opinion Summarization [60.5923941324953]
  We propose a method for unsupervised abstractive opinion summarization that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs) Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews.
  arXiv  Detail & Related papers  (2024-03-01T10:38:07Z)
• Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media [14.819625185358912]
  We introduce a hybrid abstractive summarisation approach combining hierarchical VAE with LLMs (LlaMA-2) to produce clinically meaningful summaries from social media user timelines. We assess the generated summaries via automatic evaluation against expert summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in more factual and logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.
  arXiv  Detail & Related papers  (2024-01-29T15:42:57Z)
• NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization [46.772517928718216]
  We propose a summarize-then-simplify two-stage strategy, which we call NapSS. NapSS identifies the relevant content to simplify while ensuring that the original narrative flow is preserved. Our model achieves significantly better than the seq2seq baseline on an English medical corpus.
  arXiv  Detail & Related papers  (2023-02-11T02:20:25Z)
• A Marker-based Neural Network System for Extracting Social Determinants of Health [12.6970199179668]
  Social determinants of health (SDoH) on patients' healthcare quality and the disparity is well-known. Many SDoH items are not coded in structured forms in electronic health records. We explore a multi-stage pipeline involving named entity recognition (NER), relation classification (RC), and text classification methods to extract SDoH information from clinical notes automatically.
  arXiv  Detail & Related papers  (2022-12-24T18:40:23Z)
• Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models [1.1393603788068778]
  Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models.
  arXiv  Detail & Related papers  (2022-11-14T05:39:45Z)
• Comparing Methods for Extractive Summarization of Call Centre Dialogue [77.34726150561087]
  We experimentally compare several such methods by using them to produce summaries of calls, and evaluating these summaries objectively. We found that TopicSum and Lead-N outperform the other summarisation methods, whilst BERTSum received comparatively lower scores in both subjective and objective evaluations.
  arXiv  Detail & Related papers  (2022-09-06T13:16:02Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations [0.6875312133832078]
  We bridge the gap for classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role. We present a systematic analysis in which we adapt an existing deep learning architecture to the two aforementioned tasks. The results suggest that modelling context in a hierarchical manner, which captures both word and utterance level context, yields substantial improvements on both classification tasks.
  arXiv  Detail & Related papers  (2020-07-17T04:19:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.