Tasks

A task turns a BaseDataset (raw patients) into a SampleDataset (supervised samples). Tasks define input_schema (which fields go to the model) and output_schema (the label).

samples = base.set_task(MortalityPredictionMIMIC3())

Tasks are dataset-specific. Picking the wrong combo (e.g., MortalityPredictionMIMIC3 on a MIMIC-IV dataset) will fail. Match the suffix.

Task → Dataset compatibility matrix

Mortality prediction (binary)

Task class	Dataset
`MortalityPredictionMIMIC3`	MIMIC-III
`MortalityPredictionMIMIC4`	MIMIC-IV
`InHospitalMortalityMIMIC4`	MIMIC-IV (in-hospital, narrower than next-visit)
`MortalityPredictionEICU`, `MortalityPredictionEICU2`	eICU
`MortalityPredictionOMOP`	OMOP
`MortalityPredictionStageNetMIMIC4`	MIMIC-IV (paired with StageNet model)

Readmission prediction (binary)

Task class	Dataset
`ReadmissionPredictionMIMIC3`	MIMIC-III
`ReadmissionPredictionMIMIC4`	MIMIC-IV
`ReadmissionPredictionEICU`	eICU
`ReadmissionPredictionOMOP`	OMOP

Length-of-stay prediction (multiclass)

Task class	Dataset
`LengthOfStayPredictionMIMIC3`	MIMIC-III
`LengthOfStayPredictionMIMIC4`	MIMIC-IV
`LengthOfStayPredictioneICU`	eICU
`LengthOfStayPredictionOMOP`	OMOP

LOS is bucketed into discrete classes (e.g., <1 day, 1-2 days, …, >14 days). Treat as multiclass classification.

Drug recommendation (multilabel)

Task class	Dataset
`DrugRecommendationMIMIC3`	MIMIC-III
`DrugRecommendationMIMIC4`	MIMIC-IV
`DrugRecommendationEICU`	eICU

Multilabel = each visit has a set of drugs prescribed; predict the set. Use models with drug-aware structure (GAMENet, SafeDrug, MICRON, MoleRec) or fall back to Transformer / RNN.

Specialized clinical

Task class	What it predicts
`DKAPredictionMIMIC4`	Diabetic ketoacidosis risk
`MIMIC3ICD9Coding`	ICD-9 codes for a discharge note (multilabel)

Sleep & EEG

Task class	Dataset	Predicts
`SleepStagingSleepEDF`	SleepEDF	Sleep stage (multiclass)
`EEGEventsTUEV`	TUEV	EEG events
`EEGAbnormalTUAB`	TUAB	EEG abnormality (binary)

Imaging

Task class	Dataset	Predicts
`COVID19CXRClassification`	COVID19-CXR	COVID-19 (multiclass)
`ChestXray14BinaryClassification`	ChestX-ray14	Single-disease binary
`ChestXray14MultilabelClassification`	ChestX-ray14	Multi-disease multilabel
`cardiology_isAR_fn`, `_isBBBFB_fn`, `_isAD_fn`, `_isCD_fn`, `_isWA_fn`	Cardiology	Various ECG abnormalities

Text / NLP

Task class	Dataset	Predicts
`MedicalTranscriptionsClassification`	Medical Transcriptions	Specialty/category
`DeIDNERTask`	PhysioNet DeID	De-identification NER

Genomics

Task class	Dataset	Predicts
`VariantClassificationClinVar`	ClinVar	Variant pathogenicity
`MutationPathogenicityPrediction`	COSMIC	Mutation pathogenicity
`CancerSurvivalPrediction`	TCGA-PRAD	Cancer survival
`CancerMutationBurden`	TCGA-PRAD	Tumor mutation burden

Benchmarks

Task class	Use
`BenchmarkEHRShot`	Multi-task EHR few-shot benchmark on EHRShot

Picking the right `monitor` metric

The Trainer.train(monitor=...) argument decides which checkpoint gets saved. Match it to the task type:

Task type	Good `monitor` choices
Binary (mortality, readmission, EEG abnormal)	`"pr_auc"`, `"roc_auc"`, `"f1"`
Multiclass (LOS, sleep staging, COVID CXR)	`"accuracy"`, `"f1_macro"`, `"cohen_kappa"`
Multilabel (drug rec, ICD coding, ChestXray14)	`"pr_auc_samples"`, `"jaccard_samples"`, `"f1_samples"`

Mismatched monitor (e.g., "pr_auc" on a multiclass task) silently saves the wrong epoch.

Custom tasks

When no built-in task fits, subclass BaseTask:

from pyhealth.tasks import BaseTask

class MyTask(BaseTask):
    task_name = "MyTask"
    input_schema = {"diagnoses": "sequence", "procedures": "sequence"}
    output_schema = {"label": "binary"}

    def __call__(self, patient):
        # Iterate the patient's visits, decide which become samples,
        # extract features, compute the label, and return a list of dicts.
        samples = []
        for i, visit in enumerate(patient.visits):
            if i == len(patient.visits) - 1:
                continue  # need at least one future visit for the label
            samples.append({
                "patient_id": patient.patient_id,
                "visit_id": visit.visit_id,
                "diagnoses": visit.get_code_list("DIAGNOSES_ICD"),
                "procedures": visit.get_code_list("PROCEDURES_ICD"),
                "label": int(self._compute_label(patient, visit)),
            })
        return samples

    def _compute_label(self, patient, visit): ...

The __call__ is invoked once per patient. Returning [] for a patient excludes them from the SampleDataset. The schema strings ("sequence", "binary", "multilabel", "multiclass", "regression") tell PyHealth's processors how to handle each field.