Datasets:

loolootech
/

S-ICOPD

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.


  ### 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)](https://creativecommons.org/licenses/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.
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

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}
}