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Add SepsisPredictionEICU and SepsisPredictionMIMIC4 tasks — pre-ICU window, Sepsis-3 proxy labeling

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@zhaog100 已經在處理了。

開始於 2026年3月25日。

評估

難度
5/5
預估耗時
一週以上
新手友好度
25/100
Issue 類型
功能
描述清晰度
基本清楚
活躍度
停滯
技術堆疊
python
領域
data, machine-learning

研究方向

Start by reading existing task definitions under pyhealth/tasks/, their exports in pyhealth/tasks/__init__.py, and the tests under tests/tasks/. The issue specifies the proposed eICU and MIMIC-IV inputs, label sources, and deliverables, but the configurable time windows and clinical cohort rules need to be resolved before implementation. Done means both task definitions, exports, unit tests, an example notebook, and referenced docstrings are in place.

由索引模型根據 Issue 內容生成。

描述

Summary

PyHealth currently has no sepsis prediction task. This issue proposes adding two
new task definitions:

  • SepsisPredictionEICU — eICU Collaborative Research Database
  • SepsisPredictionMIMIC4 — MIMIC-IV

Both tasks target pre-ICU / early ICU detection windows, filling a confirmed
gap identified in a PROSPERO-registered systematic review (CRD420251164609) covering
14 studies (53,795 participants): nearly all existing sepsis ML models are trained
on in-ICU data with no standardized reproducible benchmark.

Clinical Motivation

Sepsis is defined by the Sepsis-3 consensus (Singer et al., JAMA 2016) as
life-threatening organ dysfunction caused by dysregulated host response to infection.
Early detection — before ICU admission or within the first hours — is where clinical
impact is highest and where published ML models show the widest performance variance
(AUROC 0.72–0.94 across studies), largely due to inconsistent cohort definitions
and labeling strategies.

A reproducible PyHealth task enforces a consistent:

  • Observation window (configurable: default 6h from first available data)
  • Prediction gap (configurable: 0–12h before onset)
  • Sepsis label derivation (Sepsis-3 proxy: ICD codes + SOFA ≥ 2 or
    apacheadmissiondx for eICU)

Proposed Implementation

SepsisPredictionEICU
input_schema = {
    "vitals": "time_series",     # HR, RR, MAP, SpO2, Temp from vitalPeriodic
    "labs": "sequence",          # WBC, lactate, creatinine from lab table
    "conditions": "sequence",    # apacheadmissiondx / pasthistory
    "demographics": "static",    # age, gender, admissionweight
}
output_schema = {
    "sepsis_label": "binary",
}

Label derivation from eICU:

  • Positive: apacheadmissiondx containing sepsis/septic shock keywords or
    ICD codes in diagnosis table (995.91, 995.92, A41.x)
  • Negative: non-infectious admission diagnoses
  • Excluded: ambiguous/missing diagnosis records
SepsisPredictionMIMIC4
input_schema = {
    "vitals": "time_series",     # chartevents: HR, RR, MAP, SpO2, GCS
    "labs": "sequence",          # labevents: WBC, creatinine, bilirubin, lactate
    "conditions": "sequence",    # diagnoses_icd (ICD-10)
    "demographics": "static",    # age, gender, admission_type
}
output_schema = {
    "sepsis_label": "binary",
}

Label derivation from MIMIC-IV:

  • Use sepsis3 flag from mimiciv_derived.sepsis3 (already in MIMIC-IV derived
    tables) where available
  • Fallback: ICD-10 codes A40.x, A41.x in diagnoses_icd

Deliverables

  • pyhealth/tasks/sepsis_prediction_eicu.py
  • pyhealth/tasks/sepsis_prediction_mimic4.py
  • Export in pyhealth/tasks/__init__.py
  • Unit tests in tests/tasks/
  • Example notebook in examples/sepsis_prediction/
  • Docstring with clinical references

References

  • Singer M et al. The Third International Consensus Definitions for Sepsis and
    Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–810.
  • Johnson AEW et al. MIMIC-IV, a freely accessible electronic health record
    dataset. Sci Data. 2023.
  • Pollard TJ et al. The eICU Collaborative Research Database. Sci Data. 2018.
  • Sucandra et al. Time Advantage and Diagnostic Accuracy of Biomarker-Enhanced
    ML/DL for Sepsis Detection in Pre-ICU Settings. PROSPERO CRD420251164609.

Notes

Happy to implement this. Will open a PR once approach is confirmed.

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環境準備

  • 沒有 Dockerfile 或 Docker Compose 檔案
  • 沒有 Pull Request 範本
  • 閱讀貢獻指南

從這裡開始

  1. 先讀完整個 Issue，再讀專案的貢獻指南。
  2. 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
  3. Fork 儲存庫，在一個分支上完成修改。
  4. 送出 Pull Request，並在描述裡引用這個 Issue 編號。

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