[Contribution] JambaEHR: Hybrid Transformer-Mamba model for EHR prediction

docs/api/models.rst

@@ -22,6 +22,7 @@ We implement the following models for supporting multiple healthcare predictive
     models/pyhealth.models.MoleRec
     models/pyhealth.models.Deepr
     models/pyhealth.models.EHRMamba
+    models/pyhealth.models.JambaEHR
     models/pyhealth.models.ContraWR
     models/pyhealth.models.SparcNet
     models/pyhealth.models.StageNet

docs/api/models/pyhealth.models.JambaEHR.rst

@@ -0,0 +1,18 @@
+pyhealth.models.JambaEHR
+=========================
+
+Overview
+--------
+
+Hybrid Transformer-Mamba model for EHR clinical prediction, inspired by
+Jamba (AI21 Labs, ICLR 2025). Interleaves self-attention and selective
+SSM layers for each feature stream, combining attention's global context
+with SSM's linear-time efficiency for long patient histories.
+
+API Reference
+-------------
+
+.. automodule:: pyhealth.models.jamba_ehr
+    :members:
+    :undoc-members:
+    :show-inheritance:

pyhealth/models/__init__.py

@@ -7,6 +7,7 @@
 from .deepr import Deepr, DeeprLayer
 from .embedding import EmbeddingModel
 from .gamenet import GAMENet, GAMENetLayer
+from .jamba_ehr import JambaEHR, JambaLayer
 from .logistic_regression import LogisticRegression
 from .gan import GAN
 from .gnn import GAT, GCN