Implement Temporal Pointwise Convolutional (TPC) Model in Length of Stay prediction task

docs/api/models.rst

@@ -204,3 +204,4 @@ API Reference
     models/pyhealth.models.TextEmbedding
     models/pyhealth.models.BIOT
     models/pyhealth.models.unified_multimodal_embedding_docs
+    models/pyhealth.models.TPC

docs/api/models/pyhealth.models.TPC.rst

@@ -0,0 +1,14 @@
+pyhealth.models.TPC
+===================================
+
+The separate callable TPCBlock layer and the complete TPC model.
+
+.. autoclass:: pyhealth.models.tpc.TPCBlock
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. autoclass:: pyhealth.models.tpc.TPC
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/processors.rst

@@ -496,4 +496,7 @@ API Reference
     processors/pyhealth.processors.MultiHotProcessor
     processors/pyhealth.processors.StageNetProcessor
     processors/pyhealth.processors.StageNetTensorProcessor
-    processors/pyhealth.processors.GraphProcessor
+    processors/pyhealth.processors.GraphProcessor
+    processors/pyhealth.processors.RegressionSequenceProcessor
+    processors/pyhealth.processors.TPCStaticProcessor
+    processors/pyhealth.processors.TPCTimeseriesProcessor

docs/api/processors/pyhealth.processors.RegressionSequenceProcessor.rst

@@ -0,0 +1,9 @@
+pyhealth.processors.RegressionSequenceProcessor
+===================================
+
+Label processor for variable-length remaining LoS regression sequences.
+
+.. autoclass:: pyhealth.processors.RegressionSequenceProcessor
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/processors/pyhealth.processors.TPCStaticProcessor.rst

@@ -0,0 +1,9 @@
+pyhealth.processors.TPCStaticProcessor
+===================================
+
+Feature processor for TPC static inputs (demographics and admission-time measurements).
+
+.. autoclass:: pyhealth.processors.TPCStaticProcessor
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/processors/pyhealth.processors.TPCTimeseriesProcessor.rst

@@ -0,0 +1,9 @@
+pyhealth.processors.TPCTimeseriesProcessor
+===================================
+
+Feature processor for TPC time-series inputs with hourly resampling and decay indicators.
+
+.. autoclass:: pyhealth.processors.TPCTimeseriesProcessor
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/tasks.rst

@@ -229,3 +229,4 @@ Available Tasks
     Mutation Pathogenicity (COSMIC) <tasks/pyhealth.tasks.MutationPathogenicityPrediction>
     Cancer Survival Prediction (TCGA) <tasks/pyhealth.tasks.CancerSurvivalPrediction>
     Cancer Mutation Burden (TCGA) <tasks/pyhealth.tasks.CancerMutationBurden>
+    Remaining Length of Stay (TPC, MIMIC-IV) <tasks/pyhealth.tasks.RemainingLengthOfStayTPC_MIMIC4>

docs/api/tasks/pyhealth.tasks.RemainingLengthOfStayTPC_MIMIC4.rst

@@ -0,0 +1,10 @@
+pyhealth.tasks.RemainingLengthOfStayTPC_MIMIC4
+===============================================
+
+Task Classes
+------------
+
+.. autoclass:: pyhealth.tasks.remaining_length_of_stay_tpc_mimic4.RemainingLengthOfStayTPC_MIMIC4
+    :members:
+    :undoc-members:
+    :show-inheritance:

examples/mimic4_remaining_los_tpc.py

@@ -0,0 +1,168 @@
+from __future__ import annotations
+
+"""
+Minimal example: TPC remaining ICU LoS (MIMIC-IV).
+
+This is the paper-style setting:
+  - remaining ICU length-of-stay regression
+  - hourly predictions starting at hour 5
+  - MSLE loss
+"""
+
+import os
+import sys
+
+# Put cache inside repo by default (avoids sandbox permission errors).
+os.environ.setdefault("PYHEALTH_CACHE_PATH", os.path.join(os.path.dirname(__file__), "..", ".pyhealth_cache"))
+
+# Ensure we import the *local* repo `pyhealth/` rather than any site-packages install.
+_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+if _REPO_ROOT not in sys.path:
+    sys.path.insert(0, _REPO_ROOT)
+
+from pyhealth.datasets import MIMIC4EHRDataset, split_by_patient, get_dataloader
+from pyhealth.tasks import RemainingLengthOfStayTPC_MIMIC4
+from pyhealth.models import TPC
+from pyhealth.trainer import Trainer
+
+# Using Labebents and chartevents itemids reported 
+# in table 17 of the paper by Rocheteau et al. (2021)
+LABEVENTS_ITEMIDS = [
+    50861,   # Alanine Aminotransferase (ALT)
+    50863,   # Alkaline Phosphatase
+    50868,   # Anion Gap
+    50878,   # Asparate Aminotransferase (AST)
+    50882,   # Bicarbonate
+    50885,   # Bilirubin, Total
+    50893,   # Calcium, Total
+    50804,   # Calculated Total CO2
+    50902,   # Chloride        
+    50912,   # Creatinine       
+    50808,   # Free Calcium
+    50931,   # Glucose         
+    51221,   # Hematocrit
+    50810,   # Hematocrit, Calculated
+    51222,   # Hemoglobin
+    51237,   # INR(PT)
+    50813,   # Lactate
+    51248,   # MCH
+    51249,   # MCHC
+    51250,   # MCV
+    50960,   # Magnesium
+    50817,   # Oxygen Saturation
+    51274,   # PT
+    51275,   # PTT
+    50970,   # Phosphate
+    51265,   # Platelet Count
+    50971,   # Potassium
+    51277,   # RDW
+    52172,   # RDW-SD
+    51279,   # Red Blood Cells
+    50983,   # Sodium
+    51006,   # Urea Nitrogen
+    51301,   # White Blood Cells
+    50818,   # pCO2
+    50820,   # pH
+    50821,   # pO2
+]
+
+CHARTEVENTS_ITEMIDS = [
+    229319,  # Activity / Mobility (JH-HLM)
+    223876,  # Apnea Interval
+    220058,  # Arterial Blood Pressure Alarm - High
+    220056,  # Arterial Blood Pressure Alarm - Low
+    220051,  # Arterial Blood Pressure diastolic
+    220052,  # Arterial Blood Pressure mean
+    220050,  # Arterial Blood Pressure systolic
+    229323,  # Current Dyspnea Assessment
+    224639,  # Daily Weight
+    226871,  # Expiratory Ratio
+    223875,  # Fspn High
+    220739,  # GCS - Eye Opening
+    223901,  # GCS - Motor Response
+    223900,  # GCS - Verbal Response
+    225664,  # Glucose finger stick (range 70-100)
+    220045,  # Heart Rate
+    220047,  # Heart Rate Alarm - Low
+    220046,  # Heart rate Alarm - High
+    223835,  # Inspired O2 Fraction
+    224697,  # Mean Airway Pressure
+    224687,  # Minute Volume
+    220293,  # Minute Volume Alarm - High
+    220292,  # Minute Volume Alarm - Low
+    220180,  # Non Invasive Blood Pressure diastolic
+    220181,  # Non Invasive Blood Pressure mean
+    220179,  # Non Invasive Blood Pressure systolic
+    223751,  # Non-Invasive Blood Pressure Alarm - High
+    223752,  # Non-Invasive Blood Pressure Alarm - Low
+    223834,  # O2 Flow
+    223770,  # O2 Saturation Pulseoxymetry Alarm - Low
+    220277,  # O2 saturation pulseoxymetry
+    220339,  # PEEP set
+    224701,  # PSV Level
+    223791,  # Pain Level
+    224409,  # Pain Level Response
+    223873,  # Paw High
+    224695,  # Peak Insp. Pressure
+    225677,  # Phosphorous
+    224696,  # Plateau Pressure
+    224161,  # Resp Alarm - High
+    224162,  # Resp Alarm - Low
+    220210,  # Respiratory Rate
+    224688,  # Respiratory Rate (Set)
+    224690,  # Respiratory Rate (Total)
+    224689,  # Respiratory Rate (spontaneous)
+    228096,  # Richmond-RAS Scale
+    228409,  # Strength L Arm
+    228410,  # Strength L Leg
+    228412,  # Strength R Arm
+    228411,  # Strength R Leg
+    223761,  # Temperature Fahrenheit
+    224685,  # Tidal Volume (observed)
+    224684,  # Tidal Volume (set)
+    224686,  # Tidal Volume (spontaneous)
+    224700,  # Total PEEP Level
+    223849,  # Ventilator Mode
+    223874,  # Vti High
+]
+
+
+def main():
+    # Adjust these paths for your environment.
+    ehr_root = "./datasets/mimic-iv-demo/2.2"
+    cache_dir = os.path.join(_REPO_ROOT, ".pyhealth_dataset_cache")
+
+    dataset = MIMIC4EHRDataset(
+        root=ehr_root,
+        tables=["patients", "admissions", "icustays", "labevents", "chartevents"],
+        dev=True,
+        num_workers=1,
+        cache_dir=cache_dir,
+    )
+
+    task = RemainingLengthOfStayTPC_MIMIC4(
+        labevent_itemids=LABEVENTS_ITEMIDS,
+        chartevent_itemids=CHARTEVENTS_ITEMIDS,
+    )
+    sample_dataset = dataset.set_task(task)
+
+    train_ds, val_ds, test_ds = split_by_patient(sample_dataset, ratios=[0.8, 0.1, 0.1])
+    train_loader = get_dataloader(train_ds, batch_size=8, shuffle=True)
+    val_loader = get_dataloader(val_ds, batch_size=8, shuffle=False)
+    test_loader = get_dataloader(test_ds, batch_size=8, shuffle=False)
+
+    model = TPC(dataset=sample_dataset)
+    trainer = Trainer(model, metrics=["mae", "mse"])
+    trainer.train(
+        train_dataloader=train_loader,
+        val_dataloader=val_loader,
+        test_dataloader=test_loader,
+        epochs=5,
+        monitor="mae",
+        monitor_criterion="min",
+    )
+
+
+if __name__ == "__main__":
+    main()
+