diff --git a/pyhealth/tasks/__init__.py b/pyhealth/tasks/__init__.py
index fb3c6966a..d3d4afa7f 100644
--- a/pyhealth/tasks/__init__.py
+++ b/pyhealth/tasks/__init__.py
@@ -62,3 +62,4 @@
     MutationPathogenicityPrediction,
     VariantClassificationClinVar,
 )
+from .sepsis_task import sepsis_ehr_task
diff --git a/pyhealth/tasks/sepsis_task.py b/pyhealth/tasks/sepsis_task.py
new file mode 100644
index 000000000..5eda88c13
--- /dev/null
+++ b/pyhealth/tasks/sepsis_task.py
@@ -0,0 +1,58 @@
+"""
+EHR-only sepsis classification task for PyHealth. This task computes mean vital 
+sign values per visit and assigns a binary sepsis labelf ro classification.
+
+Attributes:
+    patient (Patient):
+        A PyHealth Patient object whose `data_source` attribute is a
+        Polars DataFrame containing the patient's full event history.
+Returns:
+    List[Dict[str, Any]]:
+            A list of samples. Each sample has the structure:
+
+            {
+                "patient_id": <str>,
+                "visit_id": <str>,
+                "ehr_features_mean": numpy.ndarray of shape (num_features,),
+                "y": <int binary label>
+            }
+"""
+import numpy as np
+from typing import Dict, Any, List
+from pyhealth.data import Patient
+
+
+def sepsis_ehr_task(patient: Patient) -> List[Dict[str, Any]]:
+    # Retrieve patient's event dataframe
+    df = patient.data_source
+
+    # Filter rows where event_type == "ehr"
+    ehr_df = df.filter(df["event_type"] == "ehr")
+    if ehr_df.is_empty():
+        return []
+
+    samples = []
+
+    # Unique visits for this patient
+    visit_ids = ehr_df["visit_id"].unique().to_list()
+
+    # Explicit whitelist of vital sign columns
+    vital_cols = ["heart_rate", "spo2", "glucose"]
+
+    for vid in visit_ids:
+        visit_df = ehr_df.filter(ehr_df["visit_id"] == vid)
+
+        # Select only the known vital sign columns
+        available = [c for c in vital_cols if c in visit_df.columns]
+        x = visit_df.select(available).mean().to_numpy().astype(float).flatten()
+
+        y = int(visit_df["label"][0])
+
+        samples.append({
+            "patient_id": patient.patient_id,
+            "visit_id": vid,
+            "ehr_features_mean": x,
+            "y": y,
+        })
+
+    return samples
diff --git a/tests/core/test_sepsis_task.py b/tests/core/test_sepsis_task.py
new file mode 100644
index 000000000..8209789e4
--- /dev/null
+++ b/tests/core/test_sepsis_task.py
@@ -0,0 +1,37 @@
+import numpy as np
+import polars as pl
+from pyhealth.data import Patient
+from pyhealth.tasks import sepsis_ehr_task
+
+
+def test_sepsis_ehr_task():
+    # Build test flat event-level DataFrame
+    df = pl.DataFrame({
+        "timestamp": [1, 2, 3],
+        "patient_id": ["P1", "P1", "P1"],
+        "visit_id": ["V1", "V1", "V1"],
+        "event_type": ["ehr", "ehr", "ehr"],   
+        "table": ["ehr", "ehr", "ehr"],        
+        "heart_rate": [80, 82, 85],
+        "spo2": [96, 95, 97],
+        "glucose": [120, 118, 110],
+        "label": [1, 1, 1],
+    })
+
+    # Create patient from raw event DataFrame
+    patient = Patient(
+        patient_id="P1",
+        data_source=df
+    )
+
+    # Run task
+    samples = sepsis_ehr_task(patient)
+
+    assert len(samples) == 1
+    sample = samples[0]
+
+    assert "ehr_features_mean" in sample
+    assert "y" in sample
+    assert isinstance(sample["ehr_features_mean"], np.ndarray)
+    assert sample["y"] == 1
+    assert sample["ehr_features_mean"].shape[0] == 3  # 3 vital signs