PyHealth 临床机器学习研究

references/tasks.md

The following exports were checked in PyHealth 2.0.2. A name identifies a task implementation, not proof that its cohort/outcome matches a proposed study. Inspect task.input_schema, task.output_schema and raw task(patient) output before running base.set_task(task).

Common clinical tasks

Task	Data	Key implementation details
MortalityPredictionMIMIC3, MortalityPredictionMIMIC4	MIMIC-III/IV	Current-visit conditions/procedures/drugs -> mortality: binary in next visit; requires a future admission
InHospitalMortalityMIMIC4	MIMIC-IV labs	First 48 hours' selected labs; adults and stays longer than 48 hours; labs: timeseries -> mortality: binary
MortalityPredictionEICU, MortalityPredictionEICU2, MortalityPredictionOMOP	Matching source	Inspect source-specific mortality semantics and missing labels; they are not interchangeable with MIMIC
ReadmissionPredictionMIMIC3, ReadmissionPredictionMIMIC4, ReadmissionPredictionOMOP	Matching source	readmission: binary; default window=timedelta(days=15), not a universal 30-day outcome
ReadmissionPredictionEICU	eICU	Different constructor/ICU-stay logic; no MIMIC-style window argument
LengthOfStayPredictionMIMIC3, LengthOfStayPredictionMIMIC4, LengthOfStayPredictioneICU, LengthOfStayPredictionOMOP	Matching source	los: multiclass; spelling of eICU differs from other class names
DrugRecommendationMIMIC3, DrugRecommendationMIMIC4, DrugRecommendationEICU, DrugRecommendationOMOP	Matching source	Historical inputs -> current medication set; inspect source-specific drug vocabularies
MortalityPredictionStageNetMIMIC4	MIMIC-IV	StageNet-oriented feature preparation
DKAPredictionMIMIC4	MIMIC-IV	DKA-specific cohort and label construction
MIMIC3ICD9Coding	MIMIC-III notes	Discharge-note ICD-9 coding, not early risk prediction

For MIMIC mortality/readmission/LOS, load all three selectors: diagnoses_icd, procedures_icd, prescriptions. Missing one may cause all visits to be excluded. The LOS implementation uses whole-visit codes and prescriptions: using it unchanged to claim admission-time prediction would leak future information. Its ten categories are <1 day, integer days 1 through 7, days 8 through 14, and >14; MIMIC uses whole elapsed days.

MIMIC readmission excludes the last observed admission and normally minors. The label compares next admission with current discharge using a strict < window. This selected cohort is not all discharges with complete follow-up; address censoring, transfers/overlaps, and patients without another recorded admission explicitly. The next-visit MIMIC-III mortality task maps an invalid/missing future mortality flag to zero: unknown status is not necessarily survival. Audit or override that behavior.

The MIMIC-IV in-hospital task also checks lab storetime <= prediction_time, beyond measurement time. Keep that availability filter. The MEDS mortality task has a separate contract; do not transfer its exact parameters to the MIMIC-IV task.

Signals, images, text and other tasks

Export	Dataset/task
SleepStagingSleepEDF	SleepEDF: signal: tensor -> label: multiclass; 30-second chunks by default
EEGEventsTUEV, EEGAbnormalTUAB	TUEV event / TUAB abnormality classification
COVID19CXRClassification	COVID19CXR multiclass
ChestXray14BinaryClassification, ChestXray14MultilabelClassification	One disease versus multi-disease ChestXray14 targets
cardiology_isAR_fn, cardiology_isBBBFB_fn, cardiology_isAD_fn, cardiology_isCD_fn, cardiology_isWA_fn	Legacy callable cardiology tasks; do not assume a BaseTask schema
MedicalTranscriptionsClassification, DeIDNERTask	Specialty classification / clinical de-identification
VariantClassificationClinVar, MutationPathogenicityPrediction	ClinVar/COSMIC labels
CancerSurvivalPrediction, CancerMutationBurden	TCGA-PRAD outcomes
BenchmarkEHRShot	EHRShot benchmark labels

SleepStagingSleepEDF 2.0.2 retains six labels W/N1/N2/N3/N4/REM. It does not silently merge N3 and N4 into the common five-class convention. Declare any remapping and validate EDF channels, sample rate, annotation boundaries and patient/night grouping. Other dataset recipes in this table are source-verified inventories, not executed clinical or signal experiments.

Metrics

Output mode	Example metrics / monitor names
Binary	pr_auc, roc_auc, f1
Multiclass	accuracy, f1_macro, cohen_kappa
Multilabel	pr_auc_samples, jaccard_samples, f1_samples

A metric's averaging policy matters; sample-wise multilabel metrics are not the only valid choice. Inspect rare/absent classes, label coverage and decision thresholds. PR-AUC of 0.5 has different meaning at 1% and 50% prevalence. Set the monitor to an actual validation score key and specify monitor_criterion.

Custom tasks use events in 2.x

Illustrative MIMIC-III adapter below keeps the built-in feature extraction but excludes samples whose future mortality flag is unknown. This solves only that missing-label issue; it retains the built-in next-admission cohort and feature timing.

from pyhealth.tasks import MortalityPredictionMIMIC3

class KnownNextVisitMortality(MortalityPredictionMIMIC3):
    task_name = "KnownNextVisitMortality"

    def __call__(self, patient):
        admissions = patient.get_events(event_type="admissions")
        known = {
            current.hadm_id
            for current, future in zip(admissions, admissions[1:])
            if future.hospital_expire_flag in (0, 1, "0", "1")
        }
        return [s for s in super().__call__(patient) if s["hadm_id"] in known]

For a new outcome, subclass BaseTask, declare input/output schemas, and implement __call__(patient) returning dictionaries with patient/record IDs plus all schema fields. [] excludes a patient. Use get_events(event_type=..., start=..., end=..., filters=[(...)]); the 2.x Patient has no visits or next_visit API. Define lab thresholds, units, missingness, observation time and censoring in the task itself. Give changed task implementations new cache identities or fresh cache roots.

Sources: tasks, released package.