ASCRIBE
Collection
Datasets released as a part of the contributions of "ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation" • 3 items • Updated
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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.
Terms and Conditions for Using the S-ICOPD Dataset
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). These terms apply in addition to that license.
1. Acceptance of Terms
Requesting access to or using the Dataset means you agree to these terms. If you disagree with any part, do not use the Dataset.
2. Permitted Use
- 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.
3. Data Protection and Privacy
- 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.
4. No Clinical Use
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.
5. Redistribution
- 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.
6. Attribution
- 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.
7. Access Revocation and Data Removal
- 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.
8. Disclaimer
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.
9. Limitation of Liability
Under no circumstances will the Dataset Providers be liable for any claims or damages resulting from your use of the Dataset.
10. Amendments
These terms may be updated at any time. Continued use of the Dataset signifies acceptance of the updated terms.
11. Governing Law
These terms are governed by the laws of Thailand, excluding its conflict-of-law rules.
12. Contact
Questions, privacy concerns, data-removal requests and incident reports should be sent to: contact@looloohealth.com
Consent: Requesting access to or using the Dataset signifies your acknowledgment of and agreement to these terms.
Requests are reviewed manually. Please use your institutional email and describe your intended use.
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S-ICOPD
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.
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.
Dataset Summary
| 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 |
Data Fields
Each record is one synthetic encounter.
| 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. |
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.
Uses
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.
Limitations
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.
License
Licensed under CC BY-NC 4.0 (see LICENSE). Use is also subject to the Terms and Conditions, which you accept when requesting access.
Contact
For questions, privacy concerns or data-removal requests, contact contact@looloohealth.com.
Citation
@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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