| --- |
| pretty_name: S-ICOPD |
| language: |
| - th |
| license: cc-by-nc-4.0 |
| task_categories: |
| - summarization |
| tags: |
| - medical |
| - clinical |
| - soap-note |
| - thai |
| - synthetic |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| extra_gated_heading: "Request access to S-ICOPD" |
| extra_gated_prompt: | |
| S-ICOPD contains synthetic Thai doctor–patient transcripts seeded from real, de-identified physician notes. It is licensed under **CC BY-NC 4.0**, and access is subject to the **Terms and Conditions for Using the S-ICOPD Dataset** below. Please read them before requesting access. |
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| These terms govern access to and use of the S-ICOPD dataset (the "Dataset"), released by Looloo Technology Co., Ltd. (the "Dataset Providers") with the paper *ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation* (WiNLP @ EMNLP 2026). The Dataset is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). These terms apply in addition to that license. |
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| Requesting access to or using the Dataset means you agree to these terms. If you disagree with any part, do not use the Dataset. |
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| - The Dataset is intended solely for academic, research and educational purposes in clinical language processing and medical-scribe systems, including model training, fine-tuning and evaluation. |
| - Commercial use of the Dataset without prior written permission from the Dataset Providers is forbidden. |
| - You must follow all applicable laws, regulations and research ethics, including data privacy and protection standards. |
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| - The Dataset is synthetic but seeded from real, de-identified physician notes. You must keep the Dataset confidential. |
| - Do not attempt to identify, or re-identify, any patient, physician, clinic or other individual or institution described in the Dataset, whether from the Dataset alone or in combination with other information. |
| - If you become aware that any individual could be identified from the Dataset, stop using the affected data and notify the Dataset Providers promptly (see Section 12). |
| - Store the Dataset securely, apply reasonable safeguards against unauthorized access, and limit access to individuals who have themselves accepted these terms. |
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| Do not use the Dataset, or any model or system developed with it, to make or support diagnosis, treatment or any other clinical decision about real patients. |
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| - Redistribution of the Dataset, or any portion of it, is not allowed. This includes sharing, publishing, re-hosting, selling or sublicensing it. Anyone else who wishes to use the Dataset must request access from the Dataset Providers. |
| - Sharing derived data must respect the privacy and confidentiality terms set out above. |
| - Models trained on the Dataset may be released, provided that they are not designed to reproduce, and are not distributed together with, the Dataset itself. |
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| - Cite the ASCRIBE paper in any publication or public work that uses the Dataset. |
| - You may not claim ownership of, or exclusive rights over, the Dataset or its derivatives. |
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| - Violation of these terms may result in the termination of your access to the Dataset. |
| - On termination, or on request from the Dataset Providers (including in response to a data-removal request from a participant), you must delete all copies of the Dataset, or of the affected portion, in your possession. |
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| The Dataset is provided "as is", without warranty of any kind, either express or implied, including but not limited to the accuracy or completeness of the data. |
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| Under no circumstances will the Dataset Providers be liable for any claims or damages resulting from your use of the Dataset. |
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| These terms may be updated at any time. Continued use of the Dataset signifies acceptance of the updated terms. |
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| These terms are governed by the laws of Thailand, excluding its conflict-of-law rules. |
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| Questions, privacy concerns, data-removal requests and incident reports should be sent to: **contact@looloohealth.com** |
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| **Consent:** Requesting access to or using the Dataset signifies your acknowledgment of and agreement to these terms. |
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| Requests are reviewed manually. Please use your institutional email and describe your intended use. |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Country: country |
| Intended use (briefly describe your research): text |
| I have read and agree to the Terms and Conditions for Using the S-ICOPD Dataset: checkbox |
| extra_gated_button_content: "I agree – request access" |
| --- |
| |
| # S-ICOPD |
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| [](https://arxiv.org/abs/2610.01234) |
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| S-ICOPD (Synthetic ICOPD) is a synthetic Thai training corpus for generating SOAP notes from doctor–patient conversations. Each record contains a synthetic encounter transcript, with model-generated reference atomic facts, clinical-significance labels and a reference SOAP note. |
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| It was released with the paper *ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation* (WiNLP @ EMNLP 2026), where it serves as an additional training set. |
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| ## Dataset Summary |
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| | | | |
| |---|---| |
| | Encounters | 295 (train only) | |
| | Language | Thai | |
| | Transcript length | About 980 words (4.2k characters) on average | |
| | Transcripts | Synthetic, generated by an LLM pipeline seeded from de-identified physician-written ICOPD notes. No real conversations are included. | |
| | Notes | Model-generated reference SOAP notes | |
| | Audio | Not included | |
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| ## Data Fields |
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| Each record is one synthetic encounter. |
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| | Field | Type | Description | |
| |---|---|---| |
| | `id` | int | Public record id (1–295), assigned at random. | |
| | `transcript` | list of string | The synthetic doctor–patient conversation. | |
| | `atomic_facts` | list of object | Reference atomic facts extracted from the transcript. Each has `atomic_bullet` (one clinical fact, in Thai), `medical_significance` (i.e., "clinical significance") and `evidence_note` (a short English justification). | |
| | `soap_note` | object | The reference SOAP note, as four lists of strings: `history_of_present_illness`, `physical_exam`, `results`, and `assessment_and_plan`. | |
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| All three text fields are model-generated (see the Datasets section and App. A through C of the paper for more details on the dataset generation). The data only contains the `train` split. Any dates or names present are fabricated by the model. |
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| ## Uses |
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| S-ICOPD is intended as training data for research on generating clinical notes from Thai doctor–patient conversations. What you may and may not do with it is set out in the [Terms and Conditions](TERMS.md). |
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| ## Limitations |
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| The transcripts are synthetic and may not capture the disfluencies, colloquial expressions and diversity of real clinical conversations. All texts are model-generated and may contain errors. |
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| ## License |
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| Licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (see [LICENSE](LICENSE)). Use is also subject to the [Terms and Conditions](TERMS.md), which you accept when requesting access. |
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| ## Contact |
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| For questions, privacy concerns or data-removal requests, contact contact@looloohealth.com. |
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| ## Citation |
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| ```bibtex |
| @misc{kalavantavanich2026ascribe, |
| title = {ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation}, |
| author = {Tarm Kalavantavanich and Teerawut Ponarchar and Pattaramanee Arsomngern and Jenta Wonglertsakul and Watcharakorn Chuthong and Chiraphat Boonnag and Knot Pipatsrisawat and Titipat Achakulvisut}, |
| year = {2026}, |
| eprint = {2610.01234}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL}, |
| url = {https://arxiv.org/abs/2610.01234} |
| } |
| ``` |
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|
