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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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## 1. 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**: [email protected] |
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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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## 4. 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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@article{corbeil2025empowering, |
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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 Abacha, Asma Ben and Michalopoulos, George and Swazinna, Phillip and Del-Agua, Miguel and Tremblay, Jerome and Daniel, Akila Jeeson and Bader, Cari and Cho, Yu-Cheng and Krishnan, Pooja and others}, |
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journal={arXiv preprint arXiv:2507.05517}, |
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year={2025} |
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} |
