mlsanityData to 7 Oct 2026

Learning to Ask Like a Physician

arXiv 2206.02696 ↗First posted 6 Jun 2022cs.CLcited by 16

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Abstract (arXiv)

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG: https://github.com/elehman16/discq.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 39 of its 72 references that are in the map. First posted on arXiv 6 Jun 2022.

Builds on

39 of 72 references.

References · 39

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27 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 3; 2025: 2; 2026: 1.

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  1. 2023MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Recordscited by 54
  2. 2023EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Imagescited by 53
  3. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  4. 2023Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notescited by 51
  5. 2024EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summariescited by 33
  6. 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
  7. 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?cited by 14
  8. 2023Question Answering for Electronic Health Records: A Scoping Review of datasets and modelscited by 7
  9. 2024STRUX: An LLM for Decision-Making with Structured Explanationscited by 7
  10. 2023Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Taskscited by 3
  11. 2024Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Frameworkcited by 3
  12. 2025Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertaintycited by 3
4 more
  1. 2023Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systemscited by 0
  2. 2023XAIQA: Explainer-Based Data Augmentation for Extractive Question Answeringcited by 0
  3. 2025Investigating LLM Capabilities on Long Context Comprehension for Medical Question Answeringcited by 0
  4. 2026COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completioncited by 0