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

[SHA888]

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.

[zhaog100]

Hi! I'd like to work on this bounty.

My Plan:

• Analyze the requirements
• Implement the solution
• Submit PR within 24-48 hours

Payment Address:

• Platform: OKX
• Token: USDT (TRC20)
• Address: TGu4W5T6q4KvLAbmXmZSRpUBNRCxr2aFTP

Let's get this done! 🚀

[zhaog100]

/attempt

[zhaog100]

/attempt