Automatic analysis of medical dialogue in the home hemodialysis domain: Structure induction and summarization
Open accessAbstract
Spoken medical dialogue is a valuable source of information for patients and caregivers. This work presents a first step towards automatic analysis and summarization of spoken medical dialogue. We first abstract a dialogue into a sequence of semantic categories using linguistic and contextual features integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). We then describe and implement a summarizer that utilizes this automatically induced structure. Our evaluation results indicate that automatically generated summaries exhibit high resemblance to summaries written by humans. In addition, task-based evaluation shows that physicians can reasonably answer questions related to patient care by looking at the automatically generated summaries alone, in contrast to the physicians' performance when they were given summaries from a naïve summarizer (p<0.05). This work demonstrates the feasibility of automatically structuring and summarizing spoken medical dialogue.
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ASR produces a transcript of the clinician-patient conversation that lacks clear boundaries and structure due to the unconstrained nature of conversations.
“ASR produces a transcript of the clinician–patient conversation that lacks clear boundaries and structure due to the unconstrained nature of conversations.22”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [22]
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- Authors
- Lacson, R. C., Barzilay, R., & Long, W. J.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Lacson, Ronilda C.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 39
- Issue
- 5
- Pages
- 541-555
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2021473443
- Cited by
- 38
- References
- 69
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 6 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 1 · 2015: 1 · 2014: 1
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (0415865, 0448168)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- home hemodialysis, dialogue summarization, patient-provider communication
- MeSH terms
- Artificial Intelligence, Communication, Hemodialysis, Home, Humans, Patient Education as Topic, Information Storage and Retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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