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MIMIC_PROGNOSIS

Tasks:
Question Answering
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
medical
clinical
ecg
ehr
multimodal
question-answering
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metadata 
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 Tasks Venue arXiv

UniPACT framework

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 · IEEE Xplore) — 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 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

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

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

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

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

Citation 
@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 and MIMIC-IV per their respective requirements.

Source data & construction
  • Source benchmark: MDS-ED (Alcaraz et al., 2024)
  • Underlying clinical data: MIMIC-IV-ECG and MIMIC-IV, 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.