diff --git a/pyhealth/interpret/methods/integrated_gradients.py b/pyhealth/interpret/methods/integrated_gradients.py
index 249f5a5e9..1f56eb9c1 100644
--- a/pyhealth/interpret/methods/integrated_gradients.py
+++ b/pyhealth/interpret/methods/integrated_gradients.py
@@ -166,7 +166,7 @@ class IntegratedGradients(BaseInterpreter):
         ... )
     """
 
-    def __init__(self, model: BaseModel, use_embeddings: bool = True, steps: int = 50):
+    def __init__(self, model: BaseModel, use_embeddings: bool = True, steps: int = 50, decision_threshold: float = 0.5):
         """Initialize IntegratedGradients interpreter.
 
         Args:
@@ -181,6 +181,8 @@ def __init__(self, model: BaseModel, use_embeddings: bool = True, steps: int = 5
                 approximation of the path integral. Default is 50.
                 Can be overridden in attribute() calls. More steps lead to
                 better approximation but slower computation.
+            decision_threshold: Decision threshold used when inferring the default
+                target for binary and multilabel prediction. Default is 0.5.
 
         Raises:
             AssertionError: If use_embeddings=True but model does not
@@ -193,6 +195,7 @@ def __init__(self, model: BaseModel, use_embeddings: bool = True, steps: int = 5
 
         self.use_embeddings = use_embeddings
         self.steps = steps
+        self.decision_threshold = decision_threshold
 
 
     def attribute(
@@ -217,11 +220,17 @@ def attribute(
                 the integral. If None, uses self.steps (set during
                 initialization). More steps lead to better approximation but
                 slower computation.
-            target_class_idx: Target class index for attribution.
-                For binary classification (single logit output), this is
-                a no-op because there is only one output. For multi-class
-                or multi-label, specifies which class to explain. If None,
-                uses the argmax of model output.
+            target_class_idx: Target used for attribution computation.
+                Default behavior depends on prediction mode:
+                    - binary: uses (sigmoid(logit) > decision_threshold)
+                    - multiclass: uses argmax(logits)
+                    - multilabel: uses (sigmoid(logits) > decision_threshold)
+                    Notes:
+                    - In binary mode, target_class_idx effectively behaves like a target
+                      label (0 or 1), not a class-axis index.
+                    - In multilabel mode, if target_class_idx is None, the default target is
+                      a multi-hot mask of all predicted-positive labels, and attribution is
+                      computed for the sum of those selected logits.
             **kwargs: Input data dictionary from a dataloader batch
                 containing:
                 - Feature keys (e.g., 'conditions', 'procedures'):
@@ -534,6 +543,9 @@ def _compute_target_output(
             logits: Model output logits, shape [batch, num_classes].
             target_indices: [batch] tensor of target class indices.
 
+            In multilabel mode, a multi-hot target corresponds to the sum of
+            the selected logits.
+        
         Returns:
             Scalar tensor for backpropagation.
         """