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--- |
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license: cdla-permissive-2.0 |
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task_categories: |
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- text-classification |
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- token-classification |
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language: |
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- en |
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tags: |
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- clinical |
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- doctor-patient |
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- dialog |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card: SIMORD (Simulated Medical Order Extraction Dataset) |
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## Description |
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Medical order extraction involves identifying and structuring various medical orders —such as medications, imaging studies, lab tests, and follow-ups— based on doctor-patient conversations. Previous efforts have focused on extracting entities and relations from clinical texts. |
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This dataset seeks to encourage the developement of effective solutions for improving clinical documentation, reducing the burden on providers, and ensuring critical patient information is accurately captured from long conversations. |
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The input dialogues are sourced from a combination of existing conversational datasets (e.g., ACI-Bench [1], PriMock57 [2]), and structured lists of medical orders are created by medical annotators. |
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## Dataset Summary |
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- **Name**: SIMORD |
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- **Full name / acronym**: SIMulated ORDer Extraction |
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- **Purpose / use case**: |
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SIMORD is intended to support research in extracting structured medical orders (e.g. medication orders, lab orders) from doctor-patient consultation transcripts. |
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- **Version**: As released with the EMNLP industry track paper (2025) |
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- **License / usage terms**: CDLA-2.0-permissive |
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- **Contact / Maintainer**: jcorbeil@microsoft.com |
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## Building the dataset |
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### Method 1: HF datasets |
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1. Make sure you have `datasets==3.6.0` or less, otherwise builder is not supported in recent versions. |
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2. Git clone and install requirements from `https://github.com/jpcorb20/mediqa-oe` |
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3. Add `mediqa-oe` to python path `PYTHONPATH=$PYTHONPATH:/mypath/to/mediqa_oe` (UNIX). |
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4. Run `load_dataset("microsoft/SIMORD", trust_remote_code=True)`, which will merge transcripts from ACI-Bench and Primock57 repos into the annotation files. |
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### Method 2: GitHub script |
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Follow the steps in `https://github.com/jpcorb20/mediqa-oe` to merge transcripts from ACI-Bench and Primock57 into the annotation files provided in the repo. |
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## Data Fields / Format |
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**Input fields**: |
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- **transcript** (dict of list): the doctor-patient consultation transcript as dict of three lists using those keys: |
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- `turn_id` (int): index of that turn. |
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- `speaker` (str): speaker of that turn *DOCTOR* or *PATIENT*. |
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- `transcript` (str): line of that turn. |
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**Output fields**: |
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- A JSON (or list) of **expected orders** |
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- Each order object includes at least: |
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* `order_type` (e.g. “medication”, “lab”) |
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* `description` (string) — the order text (e.g. “lasix 40 milligrams a day”) |
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* `reason` (string) — the clinical reason or indication for the order |
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* `provenance` (e.g. list of token indices or spans) — mapping back to parts of the transcript |
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## Splits |
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- `train`: examples for in-context learning or fine-tuning. |
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- `test1`: test set used for the EMNLP 2025 industry track paper. Also, previously named `dev` set for MEDIQA-OE shared task of ClinicalNLP 2025. |
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- `test2`: test set for MEDIQA-OE shared task of ClinicalNLP 2025. |
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## Citation |
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If you use this dataset, please cite: |
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@inproceedings{corbeil-etal-2025-empowering, |
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title = "Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications", |
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author = "Corbeil, Jean-Philippe and |
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Ben Abacha, Asma and |
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Michalopoulos, George and |
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Swazinna, Phillip and |
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Del-Agua, Miguel and |
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Tremblay, Jerome and |
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Daniel, Akila Jeeson and |
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Bader, Cari and |
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Cho, Kevin and |
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Krishnan, Pooja and |
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Bodenstab, Nathan and |
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Lin, Thomas and |
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Teng, Wenxuan and |
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Beaulieu, Francois and |
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Vozila, Paul", |
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editor = "Potdar, Saloni and |
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Rojas-Barahona, Lina and |
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Montella, Sebastien", |
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booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track", |
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month = nov, |
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year = "2025", |
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address = "Suzhou (China)", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2025.emnlp-industry.58/", |
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doi = "10.18653/v1/2025.emnlp-industry.58", |
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pages = "859--870", |
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ISBN = "979-8-89176-333-3" |
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} |
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## References |
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[1] Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation. Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, Meliha Yetisgen. Nature Scientific Data, 10, 586 (2023). |
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[2] PriMock57: A Dataset Of Primary Care Mock Consultations. Alex Papadopoulos Korfiatis, Francesco Moramarco, Radmila Sarac, and Aleksandar Savkov. 2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 588–598, Dublin, Ireland. |
