diff --git a/test-resources/core/mimic3demo/NOTEEVENTS.csv.gz b/test-resources/core/mimic3demo/NOTEEVENTS.csv.gz
new file mode 100644
index 000000000..3d87d0e7a
Binary files /dev/null and b/test-resources/core/mimic3demo/NOTEEVENTS.csv.gz differ
diff --git a/tests/core/test_mimic3_mortality_prediction.py b/tests/core/test_mimic3_mortality_prediction.py
index d7105782b..0fd6fc169 100644
--- a/tests/core/test_mimic3_mortality_prediction.py
+++ b/tests/core/test_mimic3_mortality_prediction.py
@@ -131,7 +131,6 @@ def test_mortality_prediction_mimic3_set_task(self):
             traceback.print_exc()
             self.fail(f"Failed to use set_task with MortalityPredictionMIMIC3: {e}")
 
-    @unittest.skip("Skipping multimodal test - noteevents not included in test resources")
     def test_multimodal_mortality_prediction_mimic3_set_task(self):
         """Test MultimodalMortalityPredictionMIMIC3 task with set_task() method."""
         task = MultimodalMortalityPredictionMIMIC3()
@@ -144,44 +143,55 @@ def test_multimodal_mortality_prediction_mimic3_set_task(self):
         self.assertIn("clinical_notes", task.input_schema)
         self.assertIn("mortality", task.output_schema)
 
+        # Load dataset with noteevents for multimodal testing
+        multimodal_dataset = MIMIC3Dataset(
+            root=self.demo_dataset_path,
+            tables=["diagnoses_icd", "procedures_icd", "prescriptions", "noteevents"],
+        )
+
         # Test using set_task method
         try:
-            sample_dataset = self.dataset.set_task(task)
+            sample_dataset = multimodal_dataset.set_task(task)
             self.assertIsNotNone(sample_dataset, "set_task should return a dataset")
-            self.assertTrue(
-                hasattr(sample_dataset, "samples"), "Sample dataset should have samples"
-            )
 
             # Verify we got some samples
-            self.assertGreater(
-                len(sample_dataset.samples), 0, "Should generate at least one sample"
-            )
+            num_samples = len(sample_dataset)
+            self.assertGreater(num_samples, 0, "Should generate at least one sample")
 
             # Test sample structure
-            if len(sample_dataset.samples) > 0:
-                sample = sample_dataset.samples[0]
-                required_keys = [
-                    "hadm_id",
-                    "patient_id",
-                    "conditions",
-                    "procedures",
-                    "drugs",
-                    "clinical_notes",
-                    "mortality",
-                ]
-                for key in required_keys:
-                    self.assertIn(key, sample, f"Sample should contain key: {key}")
+            sample = sample_dataset[0]
+            required_keys = [
+                "hadm_id",
+                "patient_id",
+                "conditions",
+                "procedures",
+                "drugs",
+                "clinical_notes",
+                "mortality",
+            ]
+            for key in required_keys:
+                self.assertIn(key, sample, f"Sample should contain key: {key}")
+
+            # Verify data types
+            self.assertIsInstance(
+                sample["clinical_notes"], str, "clinical_notes should be a string"
+            )
+            self.assertIn(
+                int(sample["mortality"]), [0, 1], "Mortality label should be 0 or 1"
+            )
 
-                # Verify data types
-                self.assertIsInstance(
-                    sample["clinical_notes"], str, "clinical_notes should be a string"
-                )
-                self.assertIn(
-                    sample["mortality"], [0, 1], "Mortality label should be 0 or 1"
-                )
+            # Verify that at least one sample has non-empty clinical notes,
+            # proving that NOTEEVENTS data was actually loaded
+            has_notes = any(
+                len(sample_dataset[i]["clinical_notes"]) > 0
+                for i in range(num_samples)
+            )
+            self.assertTrue(
+                has_notes, "At least one sample should have non-empty clinical notes"
+            )
 
-                print(f"Generated {len(sample_dataset.samples)} multimodal samples")
-                print(f"Clinical notes length: {len(sample['clinical_notes'])}")
+            print(f"Generated {num_samples} multimodal samples")
+            print(f"Clinical notes length: {len(sample['clinical_notes'])}")
 
         except Exception as e:
             self.fail(