Summary of Clinicsum: Utilizing Language Models For Generating Clinical Summaries From Patient-doctor Conversations, by Subash Neupane et al.
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
by Subash Neupane, Himanshu Tripathi, Shaswata Mitra, Sean Bozorgzad, Sudip Mittal, Shahram Rahimi, Amin Amirlatifi
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called ClinicSum is introduced to automatically generate clinical summaries from patient-doctor conversations. The architecture consists of two modules: retrieval-based filtering and inference, powered by fine-tuned Pre-trained Language Models (PLMs). A training dataset of 1,473 conversations-summaries pairs was created using publicly available datasets FigShare and MTS-Dialog, validated by Subject Matter Experts (SMEs). ClinicSum outperforms state-of-the-art PLMs in automatic evaluations and receives high preference from SMEs in human assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ClinicSum is a new way to make summaries of doctor-patient conversations. It uses special computer models to help doctors write shorter, more important reports about what happened during their meetings with patients. The model was trained on lots of real conversations between doctors and patients, and it’s better than other similar models at getting the most important information right. |
Keywords
» Artificial intelligence » Inference
