Models

All PyHealth models are PyTorch modules with a unified constructor: they take a SampleDataset (the output of base.set_task(...)) as the first argument, plus model-specific hyperparameters. The model auto-configures input/output dimensions from the dataset's schema — you don't wire layers by hand.

model = Transformer(dataset=samples, hidden_dim=128)

If you pass a BaseDataset instead of a SampleDataset, the model can't introspect schemas and will error or misbehave.

Choosing a model

Pick by data shape and task type, not by recency. The "newest" model is rarely the right answer.

EHR sequential codes (diagnoses, procedures, prescriptions across visits)

Model	When to pick it
`Transformer`	Strong default. Long visit histories, attention over codes.
`RNN` (LSTM/GRU)	Smaller datasets; faster than Transformer; sensible baseline.
`RETAIN`	When interpretability matters — produces visit-level and code-level attention weights.
`Deepr`	CNN-over-codes; readmission-style tasks.
`TCN`	Long-range temporal patterns where causality matters.
`AdaCare`	Adaptive feature extraction across irregular time intervals.
`ConCare`	Contextualized representations across visits.
`StageNet`	Disease-progression staging from irregular vitals.
`EHRMamba`	State-space alternative to Transformer for long sequences.

Drug recommendation (multilabel)

Model	When to pick it
`GAMENet`	Drug-rec baseline with memory networks; pairs with `DrugRecommendation*` tasks.
`SafeDrug`	Models drug-drug interactions / safety constraints via molecular structure.
`MICRON`	Predicts medication change between visits, not the full set.
`MoleRec`	Substructure-aware molecular drug recommendation.

Static / tabular features

Model	When to pick it
`LogisticRegression`	Strong, fast baseline. Always run this first.
`MLP`	Static numeric vectors, no sequence order.

Imaging / signals

Model	When to pick it
`CNN`	Generic convolutional baseline for images and 1D signals.
`ContraWR`	Contrastive learning for biosignals.
`SparcNet`	Sparse signal prediction (seizure, sleep staging).
`BIOT`	Biosignal transformer.

Graph-structured data

Model	When to pick it
`GNN`	Generic graph neural net baseline.
`GraphCare`	EHR codes augmented with external medical knowledge graphs (UMLS/SNOMED).
`GRASP`	Patient-similarity graph representations.

Text

Model	When to pick it
`TransformersModel`	Pretrained HuggingFace transformer (BERT-family) — clinical notes, transcripts.
`TransformerDeID`	De-identification NER head on top of a transformer.
`MedLink`	Medical entity linking.

Generative / representation

Model	When to pick it
`VAE`	Synthetic EHR generation, anomaly detection.
`GAN`	Synthetic data with adversarial training.

Reinforcement learning

Model	When to pick it
`Agent`	Treatment recommendation framed as RL.

Multimodal

Model	When to pick it
`MultimodalRNN`	Mix of sequential codes and static tensors in one sample.

Common arguments

Most clinical models accept:

dataset — the SampleDataset (required, positional)
hidden_dim — embedding/hidden width (default ≈128)
embedding_dim — separate embedding width if exposed
dropout — dropout rate
num_layers — for RNN/Transformer/TCN

Refer to the docstring (help(Transformer)) for model-specific knobs (e.g., rnn_type for RNN, num_filters for CNN, latent_dim for VAE).

Recommended progression

When starting on a new task, work up the model ladder rather than jumping to the most exotic option:

LogisticRegression — sanity check + floor.
MLP if features are static, RNN if sequential.
Transformer — strong general default.
Specialized model (RETAIN, GAMENet, StageNet, etc.) — only if the task has a property that motivates it (interpretability, drug structure, irregular time, etc.).

Stop as soon as a model does the job. A working Transformer beats a half-debugged MoleRec.

Custom models

Subclass BaseModel if nothing fits. The dataset object provides feature extractors via dataset.input_processors — use them to keep tokenization consistent with the rest of the pipeline rather than rolling custom encoders.