diff --git a/pyhealth/datasets/ehrshot.py b/pyhealth/datasets/ehrshot.py
index 6b822dc74..878295bea 100644
--- a/pyhealth/datasets/ehrshot.py
+++ b/pyhealth/datasets/ehrshot.py
@@ -20,6 +20,15 @@ class EHRShotDataset(BaseDataset):
         tables (List[str]): A list of tables to be included in the dataset.
         dataset_name (Optional[str]): The name of the dataset.
         config_path (Optional[str]): The path to the configuration file.
+
+    Examples:
+        >>> from pyhealth.datasets import EHRShotDataset
+        >>> # Load EHRShot dataset with benchmark tables
+        >>> dataset = EHRShotDataset(
+        ...     root="/path/to/ehrshot/data",
+        ...     tables=["ehrshot", "chexpert", "guo_icu", "lab_anemia"],
+        ... )
+        >>> dataset.stats()
     """
 
     def __init__(
@@ -28,7 +37,7 @@ def __init__(
         tables: List[str],
         dataset_name: Optional[str] = None,
         config_path: Optional[str] = None,
-        **kwargs
+        **kwargs,
     ) -> None:
         if config_path is None:
             logger.info("No config path provided, using default config")
@@ -38,6 +47,6 @@ def __init__(
             tables=tables,
             dataset_name=dataset_name or "ehrshot",
             config_path=config_path,
-            **kwargs
+            **kwargs,
         )
         return
diff --git a/pyhealth/datasets/mimic3.py b/pyhealth/datasets/mimic3.py
index 22ca79d5c..7e569d2f3 100644
--- a/pyhealth/datasets/mimic3.py
+++ b/pyhealth/datasets/mimic3.py
@@ -22,6 +22,15 @@ class MIMIC3Dataset(BaseDataset):
         tables (List[str]): A list of tables to be included in the dataset.
         dataset_name (Optional[str]): The name of the dataset.
         config_path (Optional[str]): The path to the configuration file.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC3Dataset
+        >>> # Load MIMIC-III dataset with clinical tables
+        >>> dataset = MIMIC3Dataset(
+        ...     root="/path/to/mimic-iii/1.4",
+        ...     tables=["diagnoses_icd", "procedures_icd", "labevents"],
+        ... )
+        >>> dataset.stats()
     """
 
     def __init__(
@@ -30,7 +39,7 @@ def __init__(
         tables: List[str],
         dataset_name: Optional[str] = None,
         config_path: Optional[str] = None,
-        **kwargs
+        **kwargs,
     ) -> None:
         """
         Initializes the MIMIC4Dataset with the specified parameters.
@@ -57,14 +66,14 @@ def __init__(
             tables=tables,
             dataset_name=dataset_name or "mimic3",
             config_path=config_path,
-            **kwargs
+            **kwargs,
         )
         return
 
     def preprocess_noteevents(self, df: pl.LazyFrame) -> pl.LazyFrame:
         """
         Table-specific preprocess function which will be called by BaseDataset.load_table().
-    
+
         Preprocesses the noteevents table by ensuring that the charttime column
         is populated. If charttime is null, it uses chartdate with a default
         time of 00:00:00.
diff --git a/pyhealth/datasets/mimic4.py b/pyhealth/datasets/mimic4.py
index 6a01033be..9d1aa55d8 100644
--- a/pyhealth/datasets/mimic4.py
+++ b/pyhealth/datasets/mimic4.py
@@ -40,6 +40,15 @@ class MIMIC4EHRDataset(BaseDataset):
         tables (List[str]): A list of tables to be included in the dataset.
         dataset_name (Optional[str]): The name of the dataset.
         config_path (Optional[str]): The path to the configuration file.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> # Load MIMIC-IV EHR dataset with clinical tables
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["diagnoses_icd", "procedures_icd", "labevents"],
+        ... )
+        >>> dataset.stats()
     """
 
     def __init__(
@@ -83,6 +92,15 @@ class MIMIC4NoteDataset(BaseDataset):
         tables (List[str]): A list of tables to be included in the dataset.
         dataset_name (Optional[str]): The name of the dataset.
         config_path (Optional[str]): The path to the configuration file.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4NoteDataset
+        >>> # Load MIMIC-IV clinical notes dataset
+        >>> dataset = MIMIC4NoteDataset(
+        ...     root="/path/to/mimic-iv-note/2.2",
+        ...     tables=["discharge", "radiology"],
+        ... )
+        >>> dataset.stats()
     """
 
     def __init__(
@@ -135,6 +153,15 @@ class MIMIC4CXRDataset(BaseDataset):
         tables (List[str]): A list of tables to be included in the dataset.
         dataset_name (Optional[str]): The name of the dataset.
         config_path (Optional[str]): The path to the configuration file.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4CXRDataset
+        >>> # Load MIMIC-CXR dataset with chest X-ray images and labels
+        >>> dataset = MIMIC4CXRDataset(
+        ...     root="/path/to/mimic-cxr/2.0.0",
+        ...     tables=["metadata", "chexpert"],
+        ... )
+        >>> dataset.stats()
     """
 
     def __init__(
@@ -217,6 +244,28 @@ class MIMIC4Dataset(BaseDataset):
         cxr_config_path: Path to the CXR config file
         dataset_name: Name of the dataset
         dev: Whether to enable dev mode (limit to 1000 patients)
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4Dataset
+        >>> # Load unified MIMIC-IV dataset with EHR, notes, and CXR data
+        >>> dataset = MIMIC4Dataset(
+        ...     ehr_root="/path/to/mimic-iv/2.2",
+        ...     note_root="/path/to/mimic-iv-note/2.2",
+        ...     cxr_root="/path/to/mimic-cxr/2.0.0",
+        ...     ehr_tables=["diagnoses_icd", "procedures_icd", "labevents"],
+        ...     note_tables=["discharge", "radiology"],
+        ...     cxr_tables=["metadata", "chexpert"],
+        ... )
+        >>> dataset.stats()
+        >>>
+        >>> # Load with only EHR and notes (without CXR)
+        >>> dataset = MIMIC4Dataset(
+        ...     ehr_root="/path/to/mimic-iv/2.2",
+        ...     note_root="/path/to/mimic-iv-note/2.2",
+        ...     ehr_tables=["diagnoses_icd", "labevents"],
+        ...     note_tables=["discharge"],
+        ... )
+        >>> dataset.stats()
     """
 
     def __init__(
diff --git a/pyhealth/tasks/benchmark_ehrshot.py b/pyhealth/tasks/benchmark_ehrshot.py
index a528b0db9..1d72ec2f1 100644
--- a/pyhealth/tasks/benchmark_ehrshot.py
+++ b/pyhealth/tasks/benchmark_ehrshot.py
@@ -6,20 +6,27 @@
 
 
 class BenchmarkEHRShot(BaseTask):
-    """Benchmark predictive tasks using EHRShot."""
+    """Benchmark predictive tasks using EHRShot.
+
+    Examples:
+        >>> from pyhealth.datasets import EHRShotDataset
+        >>> from pyhealth.tasks import BenchmarkEHRShot
+        >>> dataset = EHRShotDataset(
+        ...     root="/path/to/ehrshot/data",
+        ...     tables=["ehrshot", "splits", "guo_icu"],
+        ... )
+        >>> task = BenchmarkEHRShot(task="guo_icu")
+        >>> samples = dataset.set_task(task)
+    """
 
     tasks = {
-        "operational_outcomes": [
-            "guo_los",
-            "guo_readmission",
-            "guo_icu"
-        ],
+        "operational_outcomes": ["guo_los", "guo_readmission", "guo_icu"],
         "lab_values": [
             "lab_thrombocytopenia",
             "lab_hyperkalemia",
             "lab_hypoglycemia",
             "lab_hyponatremia",
-            "lab_anemia"
+            "lab_anemia",
         ],
         "new_diagnoses": [
             "new_hypertension",
@@ -27,11 +34,9 @@ class BenchmarkEHRShot(BaseTask):
             "new_pancan",
             "new_celiac",
             "new_lupus",
-            "new_acutemi"
+            "new_acutemi",
         ],
-        "chexpert": [
-            "chexpert"
-        ]
+        "chexpert": ["chexpert"],
     }
 
     def __init__(self, task: str, omop_tables: Optional[List[str]] = None) -> None:
@@ -53,13 +58,13 @@ def __init__(self, task: str, omop_tables: Optional[List[str]] = None) -> None:
             self.output_schema = {"label": "binary"}
         elif task in self.tasks["chexpert"]:
             self.output_schema = {"label": "multilabel"}
-  
+
     def pre_filter(self, df: pl.LazyFrame) -> pl.LazyFrame:
         if self.omop_tables is None:
             return df
         filtered_df = df.filter(
-            (pl.col("event_type") != "ehrshot") |
-            (pl.col("ehrshot/omop_table").is_in(self.omop_tables))
+            (pl.col("event_type") != "ehrshot")
+            | (pl.col("ehrshot/omop_table").is_in(self.omop_tables))
         )
         return filtered_df
 
@@ -81,9 +86,5 @@ def __call__(self, patient: Any) -> List[Dict[str, Any]]:
                 label_value = int(label_value)
                 label_value = [i for i in range(14) if (label_value >> i) & 1]
                 label_value = [13 - i for i in label_value[::-1]]
-            samples.append({
-                "feature": codes,
-                "label": label_value,
-                "split": split
-            })
+            samples.append({"feature": codes, "label": label_value, "split": split})
         return samples
diff --git a/pyhealth/tasks/bmd_hs_disease_classification.py b/pyhealth/tasks/bmd_hs_disease_classification.py
index c7c68586f..79d4a06f9 100644
--- a/pyhealth/tasks/bmd_hs_disease_classification.py
+++ b/pyhealth/tasks/bmd_hs_disease_classification.py
@@ -33,6 +33,13 @@ class BMDHSDiseaseClassification(BaseTask):
         output_schema (Dict[str, str]): The output schema specifying the output
             format. Contains:
             - "diagnosis": "multilabel"
+
+    Examples:
+        >>> from pyhealth.datasets import BMDHSDataset
+        >>> from pyhealth.tasks import BMDHSDiseaseClassification
+        >>> dataset = BMDHSDataset(root="/path/to/bmd_hs")
+        >>> task = BMDHSDiseaseClassification()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "BMDHSDiseaseClassification"
diff --git a/pyhealth/tasks/chestxray14_binary_classification.py b/pyhealth/tasks/chestxray14_binary_classification.py
index 3b28b54b2..020299ff7 100644
--- a/pyhealth/tasks/chestxray14_binary_classification.py
+++ b/pyhealth/tasks/chestxray14_binary_classification.py
@@ -16,6 +16,7 @@
 Author:
     Eric Schrock (ejs9@illinois.edu)
 """
+
 import logging
 from typing import Dict, List
 
@@ -24,6 +25,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 class ChestXray14BinaryClassification(BaseTask):
     """
     A PyHealth task class for binary classification of a specific disease
@@ -34,7 +36,15 @@ class ChestXray14BinaryClassification(BaseTask):
         input_schema (Dict[str, str]): The schema for the task input.
         output_schema (Dict[str, str]): The schema for the task output.
         disease (str): The disease label to classify.
+
+    Examples:
+        >>> from pyhealth.datasets import ChestXray14Dataset
+        >>> from pyhealth.tasks import ChestXray14BinaryClassification
+        >>> dataset = ChestXray14Dataset(root="/path/to/chestxray14")
+        >>> task = ChestXray14BinaryClassification(disease="pneumonia")
+        >>> samples = dataset.set_task(task)
     """
+
     task_name: str = "ChestXray14BinaryClassification"
     input_schema: Dict[str, str] = {"image": "image"}
     output_schema: Dict[str, str] = {"label": "binary"}
@@ -50,7 +60,8 @@ def __init__(self, disease: str) -> None:
         Raises:
             ValueError: If the specified disease is not a valid class in the dataset.
         """
-        from pyhealth.datasets import ChestXray14Dataset # Avoid circular import
+        from pyhealth.datasets import ChestXray14Dataset  # Avoid circular import
+
         if disease not in ChestXray14Dataset.classes:
             msg = f"Invalid disease: '{disease}'! Must be one of {ChestXray14Dataset.classes}."
             logger.error(msg)
diff --git a/pyhealth/tasks/chestxray14_multilabel_classification.py b/pyhealth/tasks/chestxray14_multilabel_classification.py
index 77b2b9f15..43ad3569d 100644
--- a/pyhealth/tasks/chestxray14_multilabel_classification.py
+++ b/pyhealth/tasks/chestxray14_multilabel_classification.py
@@ -16,6 +16,7 @@
 Author:
     Eric Schrock (ejs9@illinois.edu)
 """
+
 import logging
 from typing import Dict, List
 
@@ -24,6 +25,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 class ChestXray14MultilabelClassification(BaseTask):
     """
     A PyHealth task class for multilabel classification of all fourteen diseases
@@ -33,7 +35,15 @@ class ChestXray14MultilabelClassification(BaseTask):
         task_name (str): The name of the task.
         input_schema (Dict[str, str]): The schema for the task input.
         output_schema (Dict[str, str]): The schema for the task output.
+
+    Examples:
+        >>> from pyhealth.datasets import ChestXray14Dataset
+        >>> from pyhealth.tasks import ChestXray14MultilabelClassification
+        >>> dataset = ChestXray14Dataset(root="/path/to/chestxray14")
+        >>> task = ChestXray14MultilabelClassification()
+        >>> samples = dataset.set_task(task)
     """
+
     task_name: str = "ChestXray14MultilabelClassification"
     input_schema: Dict[str, str] = {"image": "image"}
     output_schema: Dict[str, str] = {"labels": "multilabel"}
@@ -54,8 +64,18 @@ def __call__(self, patient: Patient) -> List[Dict]:
         events: List[Event] = patient.get_events(event_type="chestxray14")
 
         samples = []
-        from pyhealth.datasets import ChestXray14Dataset # Avoid circular import
+        from pyhealth.datasets import ChestXray14Dataset  # Avoid circular import
+
         for event in events:
-            samples.append({"image": event["path"], "labels": [disease for disease in ChestXray14Dataset.classes if int(event[disease])]})
+            samples.append(
+                {
+                    "image": event["path"],
+                    "labels": [
+                        disease
+                        for disease in ChestXray14Dataset.classes
+                        if int(event[disease])
+                    ],
+                }
+            )
 
         return samples
diff --git a/pyhealth/tasks/covid19_cxr_classification.py b/pyhealth/tasks/covid19_cxr_classification.py
index 44c9c6776..ae6814928 100644
--- a/pyhealth/tasks/covid19_cxr_classification.py
+++ b/pyhealth/tasks/covid19_cxr_classification.py
@@ -17,6 +17,13 @@ class COVID19CXRClassification(BaseTask):
             input format. Contains a single key "image" with value "image".
         output_schema (Dict[str, str]): The output schema specifying the output
             format. Contains a single key "disease" with value "multiclass".
+
+    Examples:
+        >>> from pyhealth.datasets import COVID19CXRDataset
+        >>> from pyhealth.tasks import COVID19CXRClassification
+        >>> dataset = COVID19CXRDataset(root="/path/to/covid19_cxr")
+        >>> task = COVID19CXRClassification()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "COVID19CXRClassification"
diff --git a/pyhealth/tasks/drug_recommendation.py b/pyhealth/tasks/drug_recommendation.py
index ae0432b64..e42358299 100644
--- a/pyhealth/tasks/drug_recommendation.py
+++ b/pyhealth/tasks/drug_recommendation.py
@@ -153,6 +153,16 @@ class DrugRecommendationMIMIC4(BaseTask):
             - drugs_hist: Nested list of drug codes from history (current visit excluded)
         output_schema (Dict[str, str]): The schema for output data:
             - drugs: List of drugs to predict for current visit
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> from pyhealth.tasks import DrugRecommendationMIMIC4
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
+        ... )
+        >>> task = DrugRecommendationMIMIC4()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "DrugRecommendationMIMIC4"
diff --git a/pyhealth/tasks/in_hospital_mortality_mimic4.py b/pyhealth/tasks/in_hospital_mortality_mimic4.py
index 6835648b5..334239714 100644
--- a/pyhealth/tasks/in_hospital_mortality_mimic4.py
+++ b/pyhealth/tasks/in_hospital_mortality_mimic4.py
@@ -18,6 +18,16 @@ class InHospitalMortalityMIMIC4(BaseTask):
             - labs: A timeseries of lab results.
         output_schema (Dict[str, str]): The schema for output data, which includes:
             - mortality: A binary indicator of mortality.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> from pyhealth.tasks import InHospitalMortalityMIMIC4
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["labevents"],
+        ... )
+        >>> task = InHospitalMortalityMIMIC4()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "InHospitalMortalityMIMIC4"
diff --git a/pyhealth/tasks/length_of_stay_stagenet_mimic4.py b/pyhealth/tasks/length_of_stay_stagenet_mimic4.py
index 8a509f068..be05a22b6 100644
--- a/pyhealth/tasks/length_of_stay_stagenet_mimic4.py
+++ b/pyhealth/tasks/length_of_stay_stagenet_mimic4.py
@@ -29,6 +29,16 @@ class LengthOfStayStageNetMIMIC4(BaseTask):
     Args:
         padding: Optional padding forwarded to the StageNet processor for nested
             sequences. Default is 0.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> from pyhealth.tasks import LengthOfStayStageNetMIMIC4
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["diagnoses_icd", "procedures_icd", "labevents"],
+        ... )
+        >>> task = LengthOfStayStageNetMIMIC4()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "LengthOfStayStageNetMIMIC4"
diff --git a/pyhealth/tasks/medical_coding.py b/pyhealth/tasks/medical_coding.py
index 23b3cb2e4..739c674d1 100644
--- a/pyhealth/tasks/medical_coding.py
+++ b/pyhealth/tasks/medical_coding.py
@@ -25,6 +25,16 @@ class MIMIC3ICD9Coding(BaseTask):
         task_name: Name of the task
         input_schema: Definition of the input data schema
         output_schema: Definition of the output data schema
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC3Dataset
+        >>> from pyhealth.tasks import MIMIC3ICD9Coding
+        >>> dataset = MIMIC3Dataset(
+        ...     root="/path/to/mimic-iii/1.4",
+        ...     tables=["diagnoses_icd", "procedures_icd", "noteevents"],
+        ... )
+        >>> task = MIMIC3ICD9Coding()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "mimic3_icd9_coding"
diff --git a/pyhealth/tasks/medical_transcriptions_classification.py b/pyhealth/tasks/medical_transcriptions_classification.py
index c4e27f89a..37a84fa21 100644
--- a/pyhealth/tasks/medical_transcriptions_classification.py
+++ b/pyhealth/tasks/medical_transcriptions_classification.py
@@ -16,7 +16,17 @@ class MedicalTranscriptionsClassification(BaseTask):
         task_name (str): Name of the task
         input_schema (Dict[str, str]): Schema defining input features
         output_schema (Dict[str, str]): Schema defining output features
+
+    Examples:
+        >>> from pyhealth.datasets import MedicalTranscriptionsDataset
+        >>> from pyhealth.tasks import MedicalTranscriptionsClassification
+        >>> dataset = MedicalTranscriptionsDataset(
+        ...     root="/path/to/medical_transcriptions",
+        ... )
+        >>> task = MedicalTranscriptionsClassification()
+        >>> samples = dataset.set_task(task)
     """
+
     task_name: str = "MedicalTranscriptionsClassification"
     input_schema: Dict[str, str] = {"transcription": "text"}
     output_schema: Dict[str, str] = {"medical_specialty": "multiclass"}
diff --git a/pyhealth/tasks/mortality_prediction.py b/pyhealth/tasks/mortality_prediction.py
index b1bed4665..ba5d42977 100644
--- a/pyhealth/tasks/mortality_prediction.py
+++ b/pyhealth/tasks/mortality_prediction.py
@@ -9,6 +9,16 @@ class MortalityPredictionMIMIC3(BaseTask):
 
     This task aims to predict whether the patient will decease in the next
     hospital visit based on clinical information from the current visit.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC3Dataset
+        >>> from pyhealth.tasks import MortalityPredictionMIMIC3
+        >>> dataset = MIMIC3Dataset(
+        ...     root="/path/to/mimic-iii/1.4",
+        ...     tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
+        ... )
+        >>> task = MortalityPredictionMIMIC3()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "MortalityPredictionMIMIC3"
@@ -78,6 +88,17 @@ class MultimodalMortalityPredictionMIMIC3(BaseTask):
 
     This task aims to predict whether the patient will decease in the next
     hospital visit based on clinical information from the current visit.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC3Dataset
+        >>> from pyhealth.tasks import MultimodalMortalityPredictionMIMIC3
+        >>> dataset = MIMIC3Dataset(
+        ...     root="/path/to/mimic-iii/1.4",
+        ...     tables=["diagnoses_icd", "procedures_icd", "prescriptions",
+        ...             "noteevents"],
+        ... )
+        >>> task = MultimodalMortalityPredictionMIMIC3()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "MultimodalMortalityPredictionMIMIC3"
@@ -149,7 +170,18 @@ def __call__(self, patient: Any) -> List[Dict[str, Any]]:
 
 
 class MortalityPredictionMIMIC4(BaseTask):
-    """Task for predicting mortality using MIMIC-IV EHR data only."""
+    """Task for predicting mortality using MIMIC-IV EHR data only.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> from pyhealth.tasks import MortalityPredictionMIMIC4
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
+        ... )
+        >>> task = MortalityPredictionMIMIC4()
+        >>> samples = dataset.set_task(task)
+    """
 
     task_name: str = "MortalityPredictionMIMIC4"
     input_schema: Dict[str, str] = {
@@ -280,6 +312,21 @@ class MultimodalMortalityPredictionMIMIC4(BaseTask):
     - Lab events: 10-dimensional lab value vectors (time-series)
     - Chest X-rays: Must have an image path available
 
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4Dataset
+        >>> from pyhealth.tasks import MultimodalMortalityPredictionMIMIC4
+        >>> dataset = MIMIC4Dataset(
+        ...     ehr_root="/path/to/mimic-iv/2.2",
+        ...     note_root="/path/to/mimic-iv-note/2.2",
+        ...     cxr_root="/path/to/mimic-cxr/2.0.0",
+        ...     ehr_tables=["diagnoses_icd", "procedures_icd",
+        ...                 "prescriptions", "labevents"],
+        ...     note_tables=["discharge", "radiology"],
+        ...     cxr_tables=["metadata", "negbio"],
+        ... )
+        >>> task = MultimodalMortalityPredictionMIMIC4()
+        >>> samples = dataset.set_task(task)
+
     Patient-Level Aggregation:
         - Mortality is determined iteratively by checking if the NEXT admission
           has the death flag
@@ -373,8 +420,11 @@ def _clean_text(self, text: Optional[str]) -> Optional[str]:
         return text if text else None
 
     def _process_lab_events(
-        self, patient: Any, admission_time: datetime, admission_dischtime: datetime,
-        reference_time: Optional[datetime] = None
+        self,
+        patient: Any,
+        admission_time: datetime,
+        admission_dischtime: datetime,
+        reference_time: Optional[datetime] = None,
     ) -> Optional[tuple]:
         """Process lab events into 10-dimensional vectors with timestamps.
 
@@ -416,9 +466,7 @@ def _process_lab_events(
 
         # Parse storetime and filter (matching stagenet implementation)
         labevents_df = labevents_df.with_columns(
-            pl.col("labevents/storetime").str.strptime(
-                pl.Datetime, "%Y-%m-%d %H:%M:%S"
-            )
+            pl.col("labevents/storetime").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S")
         )
         labevents_df = labevents_df.filter(
             pl.col("labevents/storetime") <= admission_dischtime
@@ -459,9 +507,7 @@ def _process_lab_events(
                 lab_vector.append(category_value)
 
             # Calculate time from reference time (hours)
-            time_from_reference = (
-                lab_ts - reference_time
-            ).total_seconds() / 3600.0
+            time_from_reference = (lab_ts - reference_time).total_seconds() / 3600.0
 
             lab_times.append(time_from_reference)
             lab_values.append(lab_vector)
@@ -599,25 +645,23 @@ def __call__(self, patient: Any) -> List[Dict[str, Any]]:
             # Get clinical codes using hadm_id filtering
             diagnoses_icd = patient.get_events(
                 event_type="diagnoses_icd",
-                filters=[("hadm_id", "==", admission.hadm_id)]
+                filters=[("hadm_id", "==", admission.hadm_id)],
             )
             procedures_icd = patient.get_events(
                 event_type="procedures_icd",
-                filters=[("hadm_id", "==", admission.hadm_id)]
+                filters=[("hadm_id", "==", admission.hadm_id)],
             )
             prescriptions = patient.get_events(
                 event_type="prescriptions",
-                filters=[("hadm_id", "==", admission.hadm_id)]
+                filters=[("hadm_id", "==", admission.hadm_id)],
             )
 
             # Get notes using hadm_id filtering
             discharge_notes = patient.get_events(
-                event_type="discharge",
-                filters=[("hadm_id", "==", admission.hadm_id)]
+                event_type="discharge", filters=[("hadm_id", "==", admission.hadm_id)]
             )
             radiology_notes = patient.get_events(
-                event_type="radiology",
-                filters=[("hadm_id", "==", admission.hadm_id)]
+                event_type="radiology", filters=[("hadm_id", "==", admission.hadm_id)]
             )
 
             # Extract clinical codes per visit (nested structure)
@@ -627,9 +671,7 @@ def __call__(self, patient: Any) -> List[Dict[str, Any]]:
             procedures_list = self._clean_sequence(
                 [event.icd_code for event in procedures_icd]
             )
-            drugs = self._clean_sequence(
-                [event.ndc for event in prescriptions]
-            )
+            drugs = self._clean_sequence([event.ndc for event in prescriptions])
 
             # Append as nested lists (one list per visit) for nested_sequence
             all_conditions.append(conditions)
@@ -656,8 +698,10 @@ def __call__(self, patient: Any) -> List[Dict[str, Any]]:
 
             # Process lab events with reference to first admission time
             labs_data = self._process_lab_events(
-                patient, admission.timestamp, admission_dischtime,
-                reference_time=first_admission_time
+                patient,
+                admission.timestamp,
+                admission_dischtime,
+                reference_time=first_admission_time,
             )
 
             if labs_data is not None:
@@ -685,8 +729,14 @@ def __call__(self, patient: Any) -> List[Dict[str, Any]]:
         has_image = bool(image_path)
 
         # Return empty list if any required modality is missing
-        if not (has_conditions and has_procedures and has_drugs
-                and has_notes and has_labs and has_image):
+        if not (
+            has_conditions
+            and has_procedures
+            and has_drugs
+            and has_notes
+            and has_labs
+            and has_image
+        ):
             return []
 
         # Sort lab events by time and create aggregated labs data
@@ -735,6 +785,16 @@ class MortalityPredictionEICU(BaseTask):
     - using diagnosis table (ICD9CM and ICD10CM) as condition codes
     - using physicalExam table as procedure codes
     - using medication table as drugs codes
+
+    Examples:
+        >>> from pyhealth.datasets import eICUDataset
+        >>> from pyhealth.tasks import MortalityPredictionEICU
+        >>> dataset = eICUDataset(
+        ...     root="/path/to/eicu-crd/2.0",
+        ...     tables=["diagnosis", "medication", "physicalExam"],
+        ... )
+        >>> task = MortalityPredictionEICU()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "MortalityPredictionEICU"
@@ -818,6 +878,16 @@ class MortalityPredictionEICU2(BaseTask):
     Similar to MortalityPredictionEICU, but with different code mapping:
     - using admissionDx table and diagnosisString under diagnosis table as condition codes
     - using treatment table as procedure codes
+
+    Examples:
+        >>> from pyhealth.datasets import eICUDataset
+        >>> from pyhealth.tasks import MortalityPredictionEICU2
+        >>> dataset = eICUDataset(
+        ...     root="/path/to/eicu-crd/2.0",
+        ...     tables=["diagnosis", "treatment", "admissionDx"],
+        ... )
+        >>> task = MortalityPredictionEICU2()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "MortalityPredictionEICU2"
diff --git a/pyhealth/tasks/mortality_prediction_stagenet_mimic4.py b/pyhealth/tasks/mortality_prediction_stagenet_mimic4.py
index 91e1f94cd..4c0505f2d 100644
--- a/pyhealth/tasks/mortality_prediction_stagenet_mimic4.py
+++ b/pyhealth/tasks/mortality_prediction_stagenet_mimic4.py
@@ -36,6 +36,16 @@ class MortalityPredictionStageNetMIMIC4(BaseTask):
             - labs: Lab results (stagenet_tensor, 10D vectors per timestamp)
         output_schema (Dict[str, str]): The schema for output data:
             - mortality: Binary indicator (1 if any admission had mortality)
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> from pyhealth.tasks import MortalityPredictionStageNetMIMIC4
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["diagnoses_icd", "procedures_icd", "labevents"],
+        ... )
+        >>> task = MortalityPredictionStageNetMIMIC4()
+        >>> samples = dataset.set_task(task)
     """
 
     task_name: str = "MortalityPredictionStageNetMIMIC4"
diff --git a/pyhealth/tasks/readmission_prediction.py b/pyhealth/tasks/readmission_prediction.py
index 478963bac..821a7fea4 100644
--- a/pyhealth/tasks/readmission_prediction.py
+++ b/pyhealth/tasks/readmission_prediction.py
@@ -16,6 +16,16 @@ class ReadmissionPredictionMIMIC3(BaseTask):
         task_name (str): The name of the task.
         input_schema (Dict[str, str]): The schema for the task input.
         output_schema (Dict[str, str]): The schema for the task output.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC3Dataset
+        >>> from pyhealth.tasks import ReadmissionPredictionMIMIC3
+        >>> dataset = MIMIC3Dataset(
+        ...     root="/path/to/mimic-iii/1.4",
+        ...     tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
+        ... )
+        >>> task = ReadmissionPredictionMIMIC3()
+        >>> samples = dataset.set_task(task)
     """
     task_name: str = "ReadmissionPredictionMIMIC3"
     input_schema: Dict[str, str] = {"conditions": "sequence", "procedures": "sequence", "drugs": "sequence"}
@@ -123,6 +133,16 @@ class ReadmissionPredictionMIMIC4(BaseTask):
         task_name (str): The name of the task.
         input_schema (Dict[str, str]): The schema for the task input.
         output_schema (Dict[str, str]): The schema for the task output.
+
+    Examples:
+        >>> from pyhealth.datasets import MIMIC4EHRDataset
+        >>> from pyhealth.tasks import ReadmissionPredictionMIMIC4
+        >>> dataset = MIMIC4EHRDataset(
+        ...     root="/path/to/mimic-iv/2.2",
+        ...     tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
+        ... )
+        >>> task = ReadmissionPredictionMIMIC4()
+        >>> samples = dataset.set_task(task)
     """
     task_name: str = "ReadmissionPredictionMIMIC4"
     input_schema: Dict[str, str] = {"conditions": "sequence", "procedures": "sequence", "drugs": "sequence"}
@@ -364,6 +384,17 @@ class ReadmissionPredictionOMOP(BaseTask):
         task_name (str): The name of the task.
         input_schema (Dict[str, str]): The schema for the task input.
         output_schema (Dict[str, str]): The schema for the task output.
+
+    Examples:
+        >>> from pyhealth.datasets import OMOPDataset
+        >>> from pyhealth.tasks import ReadmissionPredictionOMOP
+        >>> dataset = OMOPDataset(
+        ...     root="/path/to/omop/data",
+        ...     tables=["condition_occurrence", "procedure_occurrence",
+        ...             "drug_exposure"],
+        ... )
+        >>> task = ReadmissionPredictionOMOP()
+        >>> samples = dataset.set_task(task)
     """
     task_name: str = "ReadmissionPredictionOMOP"
     input_schema: Dict[str, str] = {"conditions": "sequence", "procedures": "sequence", "drugs": "sequence"}