update examples

examples/drug_recommendation_mimic3_gamenet.py

@@ -0,0 +1,37 @@
+from pyhealth.datasets import MIMIC3Dataset
+from pyhealth.datasets import split_by_patient, get_dataloader
+from pyhealth.models import GAMENet
+from pyhealth.tasks import drug_recommendation_mimic3_fn
+from pyhealth.trainer import Trainer
+
+# STEP 1: load data
+dataset = MIMIC3Dataset(
+ root="/srv/local/data/physionet.org/files/mimiciii/1.4",
+ tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
+ code_mapping={"NDC": ("ATC", {"target_kwargs": {"level": 3}})},
+)
+print(dataset.stat())
+
+# STEP 2: set task
+dataset.set_task(drug_recommendation_mimic3_fn)
+print(dataset.stat())
+
+train_dataset, val_dataset, test_dataset = split_by_patient(dataset, [0.8, 0.1, 0.1])
+train_dataloader = get_dataloader(train_dataset, batch_size=32, shuffle=True)
+val_dataloader = get_dataloader(val_dataset, batch_size=32, shuffle=False)
+test_dataloader = get_dataloader(test_dataset, batch_size=32, shuffle=False)
+
+# STEP 3: define model
+model = GAMENet(dataset)
+
+# STEP 4: define trainer
+trainer = Trainer(model=model)
+trainer.train(
+ train_dataloader=train_dataloader,
+ val_dataloader=val_dataloader,
+ epochs=50,
+ monitor="pr_auc_samples",
+)
+
+# STEP 5: evaluate
+trainer.evaluate(test_dataloader)

examples/length_of_stay_mimic3_rnn.py

@@ -0,0 +1,43 @@
+from pyhealth.datasets import MIMIC3Dataset
+from pyhealth.datasets import split_by_patient, get_dataloader
+from pyhealth.models import RNN
+from pyhealth.tasks import length_of_stay_prediction_mimic3_fn
+from pyhealth.trainer import Trainer
+
+# STEP 1: load data
+dataset = MIMIC3Dataset(
+ root="/srv/local/data/physionet.org/files/mimiciii/1.4",
+ tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
+ code_mapping={"ICD9CM": "CCSCM", "ICD9PROC": "CCSPROC", "NDC": "ATC"},
+)
+print(dataset.stat())
+
+# STEP 2: set task
+dataset.set_task(length_of_stay_prediction_mimic3_fn)
+print(dataset.stat())
+
+train_dataset, val_dataset, test_dataset = split_by_patient(dataset, [0.8, 0.1, 0.1])
+train_dataloader = get_dataloader(train_dataset, batch_size=32, shuffle=True)
+val_dataloader = get_dataloader(val_dataset, batch_size=32, shuffle=False)
+test_dataloader = get_dataloader(test_dataset, batch_size=32, shuffle=False)
+
+# STEP 3: define model
+model = RNN(
+ dataset=dataset,
+ feature_keys=["conditions", "procedures", "drugs"],
+ label_key="label",
+ mode="multiclass",
+ operation_level="visit",
+)
+
+# STEP 4: define trainer
+trainer = Trainer(model=model)
+trainer.train(
+ train_dataloader=train_dataloader,
+ val_dataloader=val_dataloader,
+ epochs=50,
+ monitor="accuracy",
+)
+
+# STEP 5: evaluate
+trainer.evaluate(test_dataloader)

examples/medcode.py

@@ -0,0 +1,13 @@
+from pyhealth.medcode import CrossMap, InnerMap
+
+ndc = InnerMap.load("NDC")
+print("Looking up for NDC code 00597005801")
+print(ndc.lookup("00597005801"))
+
+codemap = CrossMap.load("NDC", "ATC")
+print("Mapping NDC code 00597005801 to ATC")
+print(codemap.map("00597005801"))
+
+atc = InnerMap.load("ATC")
+print("Looking up for ATC code G04CA02")
+print(atc.lookup("G04CA02"))

examples/mortality_mimic3_rnn.py

@@ -0,0 +1,43 @@
+from pyhealth.datasets import MIMIC3Dataset
+from pyhealth.datasets import split_by_patient, get_dataloader
+from pyhealth.models import RNN
+from pyhealth.tasks import mortality_prediction_mimic3_fn
+from pyhealth.trainer import Trainer
+
+# STEP 1: load data
+dataset = MIMIC3Dataset(
+ root="/srv/local/data/physionet.org/files/mimiciii/1.4",
+ tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
+ code_mapping={"ICD9CM": "CCSCM", "ICD9PROC": "CCSPROC", "NDC": "ATC"},
+)
+print(dataset.stat())
+
+# STEP 2: set task
+dataset.set_task(mortality_prediction_mimic3_fn)
+print(dataset.stat())
+
+train_dataset, val_dataset, test_dataset = split_by_patient(dataset, [0.8, 0.1, 0.1])
+train_dataloader = get_dataloader(train_dataset, batch_size=32, shuffle=True)
+val_dataloader = get_dataloader(val_dataset, batch_size=32, shuffle=False)
+test_dataloader = get_dataloader(test_dataset, batch_size=32, shuffle=False)
+
+# STEP 3: define model
+model = RNN(
+ dataset=dataset,
+ feature_keys=["conditions", "procedures", "drugs"],
+ label_key="label",
+ mode="binary",
+ operation_level="visit",
+)
+
+# STEP 4: define trainer
+trainer = Trainer(model=model)
+trainer.train(
+ train_dataloader=train_dataloader,
+ val_dataloader=val_dataloader,
+ epochs=50,
+ monitor="roc_auc",
+)
+
+# STEP 5: evaluate
+trainer.evaluate(test_dataloader)