# Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances

arXiv: https://arxiv.org/abs/2007.07151 · HTML: https://mlsanity.com/paper/2007.07151/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?](https://mlsanity.com/paper/2306.16931/) (2023)
- [FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization](https://mlsanity.com/paper/2311.02271/) (2023)
- [Improving Summarization with Human Edits](https://mlsanity.com/paper/2310.05857/) (2023)
- [Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation](https://mlsanity.com/paper/2204.10178/) (2022)
- [A Large Language Model Based Pipeline for Review of Systems Entity Recognition from Clinical Notes](https://mlsanity.com/paper/2506.11067/) (2025)

## Abstract (arXiv)

Despite diverse efforts to mine various modalities of medical data, the conversations between physicians and patients at the time of care remain an untapped source of insights. In this paper, we leverage this data to extract structured information that might assist physicians with post-visit documentation in electronic health records, potentially lightening the clerical burden. In this exploratory study, we describe a new dataset consisting of conversation transcripts, post-visit summaries, corresponding supporting evidence (in the transcript), and structured labels. We focus on the tasks of recognizing relevant diagnoses and abnormalities in the review of organ systems (RoS). One methodological challenge is that the conversations are long (around 1500 words), making it difficult for modern deep-learning models to use them as input. To address this challenge, we extract noteworthy utterances---parts of the conversation likely to be cited as evidence supporting some summary sentence. We find that by first filtering for (predicted) noteworthy utterances, we can significantly boost predictive performance for recognizing both diagnoses and RoS abnormalities.