Hang's PyHealth Hackathon Submission

examples/MISTS_by_Hang.py

@@ -0,0 +1,57 @@
+import sys
+sys.path.append('./PyHealth')
+import pyhealth
+from pyhealth.datasets import MIMIC3Dataset
+
+mimic3_ds = MIMIC3Dataset(
+        root="https://storage.googleapis.com/pyhealth/mimiciii-demo/1.4/",
+        tables=["labevents"],
+        dev=True,
+)
+
+from pyhealth.tasks import mimic3_48_ihm, MIMIC3_48_IHM
+
+samples = mimic3_ds.set_task(MIMIC3_48_IHM())
+from pyhealth.datasets import split_by_sample, get_dataloader
+
+# data split
+train_dataset, val_dataset, test_dataset = split_by_sample(samples, [0.8, 0.1, 0.1])
+
+# create dataloaders (they are <torch.data.DataLoader> object)
+train_loader = get_dataloader(train_dataset, batch_size=64, shuffle=True)
+val_loader = get_dataloader(val_dataset, batch_size=64, shuffle=False)
+test_loader = get_dataloader(test_dataset, batch_size=64, shuffle=False)
+from pyhealth.models import RNN
+
+model = RNN(
+    dataset=samples,
+    # look up what are available for "feature_keys" and "label_keys" in dataset.samples[0]
+    feature_keys=["discretized_feature"],
+    label_key="mortality",
+    mode="multiclass",
+)
+
+from pyhealth.trainer import Trainer
+
+trainer = Trainer(
+    model=model,
+    metrics=["accuracy", "f1_weighted"], # the metrics that we want to log
+    )
+
+trainer.train(
+    train_dataloader=train_loader,
+    val_dataloader=val_loader,
+    epochs=20,
+    monitor="accuracy",
+    monitor_criterion="max",
+)
+
+# option 2: use our pyhealth.metrics to evaluate
+from pyhealth.metrics.multiclass import multiclass_metrics_fn
+
+y_true, y_prob, loss = trainer.inference(test_loader)
+multiclass_metrics_fn(
+    y_true,
+    y_prob,
+    metrics=["f1_weighted", "f1_micro", "cohen_kappa"]
+)

pyhealth/data/__init__.py

@@ -1,4 +1,5 @@
 from .data import (
     Event,
     Patient,
-)
+    Visit,
+)