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README.md

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----
-license: mit
----
+---
+license: pddl
+license_name: physionet-credentialed-health-data-license-1.5.0
+license_link: https://physionet.org/content/mimiciv/view-license/3.1/
+language:
+- en
+tags:
+- medical
+- clinical
+- ecg
+- ehr
+- multimodal
+- question-answering
+- prognosis
+- mimic-iv
+task_categories:
+- question-answering
+size_categories:
+- 100K<n<1M
+---
+
+# UniPACT-MDS-ED Prompted Dataset
+
+![Modality](https://img.shields.io/badge/modality-ECG_%2B_EHR-blue)
+![Tasks](https://img.shields.io/badge/sub--tasks-1443-green)
+![Venue](https://img.shields.io/badge/ICASSP-2026-b31b1b)
+![arXiv](https://img.shields.io/badge/arXiv-2601.17916-b31b1b)
+
+![UniPACT framework](./UniPACT_Model.png)
+
+A multimodal prompt-formatted version of the **MDS-ED** prognostic benchmark (built on MIMIC-IV-ECG + MIMIC-IV), used to train and evaluate **UniPACT**. Each example pairs a raw 12-lead ECG waveform with a natural-language prompt that textualizes structured EHR (demographics, biometrics, vital signs) plus a binary clinical question (Yes/No).
+
+📄 **Paper:** Tang, Xia, Lu, Saeed. *UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR.* ICASSP 2026 ([arXiv:2601.17916](https://arxiv.org/abs/2601.17916) · [IEEE Xplore](https://ieeexplore.ieee.org/document/11461333)) — read this for architecture, training recipe, ablations, and full results.
+
+## 🔑 Access
+
+This dataset inherits the **PhysioNet Credentialed Health Data License** from MIMIC-IV. Before you can download:
+
+1. Create a [PhysioNet](https://physionet.org/) account
+2. Complete the required CITI "Data or Specimens Only Research" training
+3. Sign the MIMIC-IV data use agreement
+
+Without credentialing, the files will not be accessible.
+
+## 📊 Task coverage
+
+The dataset spans **1443 binary prognostic sub-tasks** across four families:
+
+| Family | Sub-tasks | Description |
+|---|---:|---|
+| Diagnosis | 1428 | Disease-level binary diagnosis classes |
+| Deterioration | 6 | Acute clinical deterioration outcomes |
+| ICU admission | 2 | ICU-related admission outcomes |
+| Mortality | 7 | Mortality at multiple time horizons |
+
+Splits follow the official MDS-ED train / validation / test partition.
+
+## 🧪 Examples
+
+Each row contains a raw ECG tensor (`(5000, 12)` float array · 10 s @ 500 Hz · 12 leads), a textualized EHR prompt, a binary clinical question, and a Yes/No label. One real sample from each task family:
+
+**Deterioration** — `deterioration_severe_hypoxemia_362`
+```text
+ECG:  …/p16463772/s48979490/48979490
+EHR:  54 y/o BLACK AFRICAN AMERICAN female; BMI 29.8, weight 76.2;
+      temp 36.4, HR 60, RR 16.5, SpO₂ 100, BP 153/94, acuity 2.0.
+Q:    Will the patient "experience severe hypoxemia"?
+A:    Yes
+```
+
+**ICU admission** — `deterioration_icu_24h_7`
+```text
+ECG:  …/p18585855/s44869927/44869927
+EHR:  54 y/o WHITE female; BMI 28.9, weight 71.7, height 157.5;
+      temp 36.8, HR 77.5, RR 16.5, SpO₂ 99.5, BP 115.5/66.5, acuity 3.0.
+Q:    Will the patient "require ICU admission within the next 24 hours"?
+A:    Yes
+```
+
+**Mortality** — `deterioration_mortality_1d_3`
+```text
+ECG:  …/p13953606/s48605173/48605173
+EHR:  61 y/o WHITE male; BMI 28.7, weight 104.1, height 190.5;
+      temp 36.3, HR 101, RR 20, SpO₂ 99, BP 156/81, acuity 1.0.
+Q:    Will the patient "die within 24 hours"?
+A:    No
+```
+
+**Diagnosis** — `diagnoses_i10_0`
+```text
+ECG:  …/p11922120/s41055417/41055417
+EHR:  66 y/o WHITE male; BMI 31.7, weight 118.1;
+      temp 36.3, HR 81, RR 18, SpO₂ 99, BP 141/73, acuity 2.0.
+Q:    Will the patient be diagnosed with "Essential (primary) hypertension"?
+A:    No
+```
+
+---
+
+## Reference
+
+<details>
+<summary><b>Citation</b></summary>
+
+```bibtex
+@INPROCEEDINGS{11461333,
+  author    = {Tang, Jialu and Xia, Tong and Lu, Yuan and Saeed, Aaqib},
+  booktitle = {ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  title     = {UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR},
+  year      = {2026},
+  pages     = {22537-22541},
+  doi       = {10.1109/ICASSP55912.2026.11461333}
+}
+```
+
+Please also cite the underlying [MDS-ED benchmark](https://arxiv.org/abs/2407.17856) and [MIMIC-IV](https://physionet.org/content/mimiciv/) per their respective requirements.
+
+</details>
+
+<details>
+<summary><b>Source data & construction</b></summary>
+
+- **Source benchmark:** [MDS-ED](https://arxiv.org/abs/2407.17856) (Alcaraz et al., 2024)
+- **Underlying clinical data:** [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/) and [MIMIC-IV](https://physionet.org/content/mimiciv/), collected at Beth Israel Deaconess Medical Center and de-identified under HIPAA Safe Harbor by the MIT Laboratory for Computational Physiology
+- **EHR textualization:** 3 demographic + 3 biometric + 7 vital-parameter fields are inserted into fixed sentence templates
+- **Labels:** derived programmatically from existing structured fields (ICD codes, ICU stay tables, mortality timestamps, MDS-ED deterioration outcomes) — no human re-annotation
+- **ECG:** raw 12-lead waveforms kept in native form for waveform encoders; no conversion to text reports
+
+See paper §2 for the full construction pipeline.
+
+</details>