--- dataset_info: features: - name: id dtype: string - name: transcript dtype: string - name: enumerated_transcript dtype: string - name: orders list: - name: description dtype: string - name: order_type dtype: string - name: provenance list: int64 - name: reason dtype: string splits: - name: train num_bytes: 1152941 num_examples: 81 - name: test1 num_bytes: 1327621 num_examples: 90 - name: test2 num_bytes: 1329727 num_examples: 92 download_size: 1885629 dataset_size: 3810289 configs: - config_name: default data_files: - split: train path: data/train-* - split: test1 path: data/test1-* - split: test2 path: data/test2-* license: cdla-permissive-2.0 --- # SIMORD HuggingFace re-upload of the [SIMORD dataset](https://huggingface.co/datasets/microsoft/SIMORD), **a medical order extraction benchmark based on doctor-patient conversations**, with corrections to data splits and all text transcripts now included by default. If used, please cite the original authors using the citation below. ## Dataset Details ### Dataset Sources - **HuggingFace:** https://huggingface.co/datasets/microsoft/SIMORD - **Paper:** https://arxiv.org/pdf/2507.05517 ### Dataset Description The dataset contains three splits (with their corresponding original SIMORD files): 1) `train` (from `train.json`): examples for in-context learning or fine-tuning. 2) `test1` (from `dev.json`): test set used for the EMNLP 2025 industry track paper. 3) `test2` (from `test.json`): test set for MEDIQA-OE shared task of ClinicalNLP 2025. With the following distribution | Split | Original | New | Change | | :--- | :---: | :---: | :---: | | `train` | 63 | 81 | +18 | | `test1` | 100 | 90 | -10 | | `test2` | 100 | 92 | -8 | | **TOTAL** | **263** | **263** | **-** | Note: Both the original SIMORD dataset and this upload use the split name `test1` instead of dev/validation (even though the file is `dev.json`) and `test2` instead of test (even though the file is `test.json`), since both were used as test sets. Each sample contains the following fields: - `id`: unique sample identifier - `transcript`: the full doctor-patient conversation text - `enumerated_transcript`: the transcript with 1-based line numbers before each speaker turn (e.g., "1 [doctor] hello\n2 [patient] hi") - `orders`: list of expected medical orders, each with `order_type`, `description`, `reason`, and `provenance` (line numbers in enumerated_transcript) Note: The original SIMORD dataset only contains `id` and `expected_orders`, requiring users to separately load transcripts from ACI-Bench or PriMock57. This version includes the transcripts directly and fixes provenance line numbers to match the enumerated_transcript format. ### Dataset Changes #### Data Splits The SIMORD dataset is derived from both [ACI-Bench](https://github.com/wyim/aci-bench) and [PriMock57](https://github.com/babylonhealth/primock57). While PriMock57 doesn't contain any explicit data splits, ACI-Bench contains five splits: `train`, `valid`, `test1`, `test2`, and `test3`. As discussed in an [open HF issue](https://huggingface.co/datasets/microsoft/SIMORD/discussions/2), these splits were not respected when being merged into SIMORD. For example, SIMORD's `test.json` contains an ACI-Bench train sample: ` "id": "acibench_D2N036_aci_train" ` The official SIMORD HF upload contains three data files that are mapped to the following splits | SIMORD File | Mapped Split | Total | ACI-Train | ACI-Valid/Dev | ACI-Test1 | ACI-Test2 | ACI-Test3 | PriMock57 | |:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [train.json](https://huggingface.co/datasets/microsoft/SIMORD/blob/main/data/train.json) | `train` | 63 | 15 | 8 | 8 | 10 | 8 | 14 | | [dev.json](https://huggingface.co/datasets/microsoft/SIMORD/blob/main/data/dev.json) | `test1` | 100 | 27 | 3 | 20 | 14 | 13 | 23 | | [test.json](https://huggingface.co/datasets/microsoft/SIMORD/blob/main/data/test.json) | `test2` | 100 | 25 | 9 | 11 | 16 | 19 | 20 | This updated version of SIMORD reallocates samples using the following logic: - **New `train`** = old train (train+PriMock57 samples) + old test1 (train samples) + old test2 (train samples) - **New `test1`** = old test1 (non-train samples) + half of old train (non-train, non-PriMock57 samples) - **New `test2`** = old test2 (non-train samples) + half of old train (non-train, non-PriMock57 samples) In other words: - Samples with `_train` suffix are moved to `train`, regardless of which original file they came from - PriMock57 samples stay in their original splits, since PriMock57 has no explicit data splits - Non-train samples in the original `test1` and `test2` splits stay where they are - Non-train, non-PriMock57 samples that were misplaced in the original `train` split are evenly distributed between `test1` and `test2` After reallocation, the new splits contain the following counts: | New Split | Total | ACI-Train | ACI-Valid/Dev | ACI-Test1 | ACI-Test2 | ACI-Test3 | PriMock57 | |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | `train` | 81 | 67 | 0 | 0 | 0 | 0 | 14 | | `test1` | 90 | 0 | 7 | 24 | 19 | 17 | 23 | | `test2` | 92 | 0 | 13 | 15 | 21 | 23 | 20 | #### Provenance Corrections The `orders` column includes a `reason` field within the JSON object, which is a typically a short excerpt from the `transcript` justifying the order. The JSON object also includes a `provenance` field, which is a list of line number indices where the different text spans in the `reason` field are extracted from. In the original SIMORD dataset, the provenance line numbers usually exceed the total number of speaker turns in the transcript. This likely reflects some preprocessing scheme used prior to publication, or perhaps has something to do with prepending prompts before each transcript. In any case, to align the provenances to the raw transcripts, we: 1. Use the `reason` field (which contains text extracted from the transcript) to find the all relevant line numbers 2. Calculate the offset between the original provenance and the actual line numbers 3. Shift all provenances to match the new `enumerated_transcript` line numbers > Note: not every `reason` field is directly extracted from the transcript, so there are still some incorrect `provenance` values For example, `acibench_virtassist_train_D2N010` only has 61 speaker turns, but the original dataset had `provenance` values like [91, 97], which are out of range. ```json { "description": "amoxicillin 500 milligrams three times a day 10 day", "order_type": "medication", "provenance": [91, 97], "reason": "positive for strep" } ``` However, we see that this particular order corresponds to text in line 45 of the transcript. ``` ... 45 [doctor] ..., uh , positive for strep . so i think we have some reasons... ... 51 [doctor] yes it is , yeah . ... ``` So, the offset is 45 - 91 = -46, and the `provenance` shifts from [91, 97] to [45, 51]: ```json { "description": "amoxicillin 500 milligrams three times a day 10 day", "order_type": "medication", "provenance": [45, 51], "reason": "positive for strep" } ``` ### Direct Use ```python import json from datasets import load_dataset if __name__ == "__main__": # load all data dataset = load_dataset("mkieffer/SIMORD") # load only train split dataset_train = load_dataset("mkieffer/SIMORD", split="train") # load only test1 split dataset_test1 = load_dataset("mkieffer/SIMORD", split="test1") print("\nfull dataset:\n", dataset) print("\ntrain split:\n", dataset_train) print("\ntest1 split:\n", dataset_test1) print("\ntrain sample:\n", json.dumps(dataset_train[0], indent=2)) print("\ntest1 sample:\n", json.dumps(dataset_test1[0], indent=2)) ``` ## Citation ```bibtex @inproceedings{corbeil-etal-2025-empowering, title = "Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications", author = "Corbeil, Jean-Philippe and Ben Abacha, Asma and Michalopoulos, George and Swazinna, Phillip and Del-Agua, Miguel and Tremblay, Jerome and Daniel, Akila Jeeson and Bader, Cari and Cho, Kevin and Krishnan, Pooja and Bodenstab, Nathan and Lin, Thomas and Teng, Wenxuan and Beaulieu, Francois and Vozila, Paul", editor = "Potdar, Saloni and Rojas-Barahona, Lina and Montella, Sebastien", booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track", month = nov, year = "2025", address = "Suzhou (China)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.emnlp-industry.58/", doi = "10.18653/v1/2025.emnlp-industry.58", pages = "859--870", ISBN = "979-8-89176-333-3" } ```