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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'gender', 'race', 'encounter_id', 'age'}) and 114 missing columns ({'glyburide_metformin_Up', 'race_Hispanic', 'number_emergency_mean', 'A1Cresult_is_missing', 'number_outpatient', 'number_inpatient_sum', 'weight_25_50)', 'weight_125_150)', 'weight_150_175)', 'payer_code_is_missing', 'gender_Male', 'repaglinide_Up', 'insulin_Up', 'A1Cresult_Norm', 'repaglinide_No', 'pioglitazone_No', 'care_utilization_risk_score', 'bmi_overweight', 'insulin_Steady', 'glimepiride_pioglitazone_Steady', 'glimepiride_Steady', 'metformin_Up', 'number_outpatient_max', 'number_emergency', 'weight_is_missing', 'race_Caucasian', 'medical_specialty_is_missing', 'diag_2_cat_target_encoded', 'pioglitazone_Up', 'glyburide_metformin_Steady', 'diag_3_cat_target_encoded', 'race_Asian', 'weight_175_200)', 'med_diagnosis_interaction', 'glipizide_metformin_Steady', 'chlorpropamide_No', 'insulin_No', 'A1Cresult_8', 'age_bucket', 'number_inpatient_max', 'rosiglitazone_Up', 'acarbose_Up', 'glyburide_Steady', 'los_medication_interaction', 'nateglinide_Steady', 'glyburide_No', 'chlorpropamide_Up', 'number_emergency_max', 'bmi_underweight', 'nateglinide_No', 'medical_specialty_target_encoded', 'diag_3_target_encoded', 'total_visits', 'bmi_normal', 'medication_complexity_score', 'gender_Unknown_Invalid', 'diag_1_cat_target_encoded', 'bmi_obese', 'max_glu_serum_300', 'admission_type_id', 'tolazamide_Steady', 'number_outpatient_sum', 'chlorpropamide_Steady', 'race_is_missing', 'time_in_hospital', 'miglitol_No', 'num_lab_procedures', 'race_Other', 'glipizide_Steady', 'discharge_disposition_id', 'miglitol_Steady', 'metformin_pioglitazone_Steady', 'glimepiride_Up', 'number_inpatient', 'nateglinide_Up', 'diabetesMed_Yes', 'age_target_encoded', 'glyburide_Up', 'pioglitazone_Steady', 'miglitol_Up', 'weight_50_75)', 'metformin_Steady', 'number_inpatient_mean', 'glipizide_No', 'tolazamide_Up', 'diag_1_target_encoded', 'num_medications', 'max_glu_serum_Norm', 'number_diagnoses', 'target', 'metformin_rosiglitazone_Steady', 'change_No', 'repaglinide_Steady', 'number_emergency_sum', 'rosiglitazone_Steady', 'num_procedures', 'tolbutamide_Steady', 'admission_source_id', 'rosiglitazone_No', 'glyburide_metformin_No', 'diag_2_target_encoded', 'number_outpatient_mean', 'acetohexamide_Steady', 'weight_100_125)', 'metformin_No', 'unique_diagnosis_categories', 'weight_75_100)', 'acarbose_No', 'glimepiride_No', 'acarbose_Steady', 'payer_code_target_encoded', 'glipizide_Up', 'weight_0_25)', 'troglitazone_Steady'}).

This happened while the csv dataset builder was generating data using

hf://datasets/auphong2707/hospital-readmission-risk-data/splits/train_demographics.csv (at revision 4a61a3591f079628c72f5edbc7494ca7dda8741d)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              race: string
              gender: string
              age: string
              encounter_id: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 710
              to
              {'admission_type_id': Value('float64'), 'discharge_disposition_id': Value('float64'), 'admission_source_id': Value('float64'), 'time_in_hospital': Value('float64'), 'num_lab_procedures': Value('float64'), 'num_procedures': Value('float64'), 'num_medications': Value('float64'), 'number_outpatient': Value('float64'), 'number_emergency': Value('float64'), 'number_inpatient': Value('float64'), 'number_diagnoses': Value('float64'), 'A1Cresult_is_missing': Value('float64'), 'weight_is_missing': Value('float64'), 'race_is_missing': Value('float64'), 'medical_specialty_is_missing': Value('float64'), 'payer_code_is_missing': Value('float64'), 'unique_diagnosis_categories': Value('float64'), 'number_outpatient_mean': Value('float64'), 'number_outpatient_max': Value('float64'), 'number_outpatient_sum': Value('float64'), 'number_emergency_mean': Value('float64'), 'number_emergency_max': Value('float64'), 'number_emergency_sum': Value('float64'), 'number_inpatient_mean': Value('float64'), 'number_inpatient_max': Value('float64'), 'number_inpatient_sum': Value('float64'), 'total_visits': Value('float64'), 'medication_complexity_score': Value('float64'), 'care_utilization_risk_score': Value('float64'), 'age_bucket': Value('float64'), 'bmi_underweight': Value('float64'), 'bmi_normal': Value('float64'), 'bmi_overweight': Value('float64'), 'bmi_obese': Value('float64'), 'los_medication_interaction': Value('float64'), 'med_diagnosis_interaction': Value('float64'), 'race_Asian': Value('float64')
              ...
              , 'pioglitazone_No': Value('float64'), 'pioglitazone_Steady': Value('float64'), 'pioglitazone_Up': Value('float64'), 'rosiglitazone_No': Value('float64'), 'rosiglitazone_Steady': Value('float64'), 'rosiglitazone_Up': Value('float64'), 'acarbose_No': Value('float64'), 'acarbose_Steady': Value('float64'), 'acarbose_Up': Value('float64'), 'miglitol_No': Value('float64'), 'miglitol_Steady': Value('float64'), 'miglitol_Up': Value('float64'), 'troglitazone_Steady': Value('float64'), 'tolazamide_Steady': Value('float64'), 'tolazamide_Up': Value('float64'), 'insulin_No': Value('float64'), 'insulin_Steady': Value('float64'), 'insulin_Up': Value('float64'), 'glyburide_metformin_No': Value('float64'), 'glyburide_metformin_Steady': Value('float64'), 'glyburide_metformin_Up': Value('float64'), 'glipizide_metformin_Steady': Value('float64'), 'glimepiride_pioglitazone_Steady': Value('float64'), 'metformin_rosiglitazone_Steady': Value('float64'), 'metformin_pioglitazone_Steady': Value('float64'), 'change_No': Value('float64'), 'diabetesMed_Yes': Value('float64'), 'age_target_encoded': Value('float64'), 'payer_code_target_encoded': Value('float64'), 'medical_specialty_target_encoded': Value('float64'), 'diag_1_target_encoded': Value('float64'), 'diag_2_target_encoded': Value('float64'), 'diag_3_target_encoded': Value('float64'), 'diag_1_cat_target_encoded': Value('float64'), 'diag_2_cat_target_encoded': Value('float64'), 'diag_3_cat_target_encoded': Value('float64'), 'target': Value('int64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'gender', 'race', 'encounter_id', 'age'}) and 114 missing columns ({'glyburide_metformin_Up', 'race_Hispanic', 'number_emergency_mean', 'A1Cresult_is_missing', 'number_outpatient', 'number_inpatient_sum', 'weight_25_50)', 'weight_125_150)', 'weight_150_175)', 'payer_code_is_missing', 'gender_Male', 'repaglinide_Up', 'insulin_Up', 'A1Cresult_Norm', 'repaglinide_No', 'pioglitazone_No', 'care_utilization_risk_score', 'bmi_overweight', 'insulin_Steady', 'glimepiride_pioglitazone_Steady', 'glimepiride_Steady', 'metformin_Up', 'number_outpatient_max', 'number_emergency', 'weight_is_missing', 'race_Caucasian', 'medical_specialty_is_missing', 'diag_2_cat_target_encoded', 'pioglitazone_Up', 'glyburide_metformin_Steady', 'diag_3_cat_target_encoded', 'race_Asian', 'weight_175_200)', 'med_diagnosis_interaction', 'glipizide_metformin_Steady', 'chlorpropamide_No', 'insulin_No', 'A1Cresult_8', 'age_bucket', 'number_inpatient_max', 'rosiglitazone_Up', 'acarbose_Up', 'glyburide_Steady', 'los_medication_interaction', 'nateglinide_Steady', 'glyburide_No', 'chlorpropamide_Up', 'number_emergency_max', 'bmi_underweight', 'nateglinide_No', 'medical_specialty_target_encoded', 'diag_3_target_encoded', 'total_visits', 'bmi_normal', 'medication_complexity_score', 'gender_Unknown_Invalid', 'diag_1_cat_target_encoded', 'bmi_obese', 'max_glu_serum_300', 'admission_type_id', 'tolazamide_Steady', 'number_outpatient_sum', 'chlorpropamide_Steady', 'race_is_missing', 'time_in_hospital', 'miglitol_No', 'num_lab_procedures', 'race_Other', 'glipizide_Steady', 'discharge_disposition_id', 'miglitol_Steady', 'metformin_pioglitazone_Steady', 'glimepiride_Up', 'number_inpatient', 'nateglinide_Up', 'diabetesMed_Yes', 'age_target_encoded', 'glyburide_Up', 'pioglitazone_Steady', 'miglitol_Up', 'weight_50_75)', 'metformin_Steady', 'number_inpatient_mean', 'glipizide_No', 'tolazamide_Up', 'diag_1_target_encoded', 'num_medications', 'max_glu_serum_Norm', 'number_diagnoses', 'target', 'metformin_rosiglitazone_Steady', 'change_No', 'repaglinide_Steady', 'number_emergency_sum', 'rosiglitazone_Steady', 'num_procedures', 'tolbutamide_Steady', 'admission_source_id', 'rosiglitazone_No', 'glyburide_metformin_No', 'diag_2_target_encoded', 'number_outpatient_mean', 'acetohexamide_Steady', 'weight_100_125)', 'metformin_No', 'unique_diagnosis_categories', 'weight_75_100)', 'acarbose_No', 'glimepiride_No', 'acarbose_Steady', 'payer_code_target_encoded', 'glipizide_Up', 'weight_0_25)', 'troglitazone_Steady'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/auphong2707/hospital-readmission-risk-data/splits/train_demographics.csv (at revision 4a61a3591f079628c72f5edbc7494ca7dda8741d)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

admission_type_id
float64
discharge_disposition_id
float64
admission_source_id
float64
time_in_hospital
float64
num_lab_procedures
float64
num_procedures
float64
num_medications
float64
number_outpatient
float64
number_emergency
float64
number_inpatient
float64
number_diagnoses
float64
A1Cresult_is_missing
float64
weight_is_missing
float64
race_is_missing
float64
medical_specialty_is_missing
float64
payer_code_is_missing
float64
unique_diagnosis_categories
float64
number_outpatient_mean
float64
number_outpatient_max
float64
number_outpatient_sum
float64
number_emergency_mean
float64
number_emergency_max
float64
number_emergency_sum
float64
number_inpatient_mean
float64
number_inpatient_max
float64
number_inpatient_sum
float64
total_visits
float64
medication_complexity_score
float64
care_utilization_risk_score
float64
age_bucket
float64
bmi_underweight
float64
bmi_normal
float64
bmi_overweight
float64
bmi_obese
float64
los_medication_interaction
float64
med_diagnosis_interaction
float64
race_Asian
float64
race_Caucasian
float64
race_Hispanic
float64
race_Other
float64
gender_Male
float64
gender_Unknown_Invalid
float64
weight_0_25)
float64
weight_100_125)
float64
weight_125_150)
float64
weight_150_175)
float64
weight_175_200)
float64
weight_25_50)
float64
weight_50_75)
float64
weight_75_100)
float64
max_glu_serum_300
float64
max_glu_serum_Norm
float64
A1Cresult_8
float64
A1Cresult_Norm
float64
metformin_No
float64
metformin_Steady
float64
metformin_Up
float64
repaglinide_No
float64
repaglinide_Steady
float64
repaglinide_Up
float64
nateglinide_No
float64
nateglinide_Steady
float64
nateglinide_Up
float64
chlorpropamide_No
float64
chlorpropamide_Steady
float64
chlorpropamide_Up
float64
glimepiride_No
float64
glimepiride_Steady
float64
glimepiride_Up
float64
acetohexamide_Steady
float64
glipizide_No
float64
glipizide_Steady
float64
glipizide_Up
float64
glyburide_No
float64
glyburide_Steady
float64
glyburide_Up
float64
tolbutamide_Steady
float64
pioglitazone_No
float64
pioglitazone_Steady
float64
pioglitazone_Up
float64
rosiglitazone_No
float64
rosiglitazone_Steady
float64
rosiglitazone_Up
float64
acarbose_No
float64
acarbose_Steady
float64
acarbose_Up
float64
miglitol_No
float64
miglitol_Steady
float64
miglitol_Up
float64
troglitazone_Steady
float64
tolazamide_Steady
float64
tolazamide_Up
float64
insulin_No
float64
insulin_Steady
float64
insulin_Up
float64
glyburide_metformin_No
float64
glyburide_metformin_Steady
float64
glyburide_metformin_Up
float64
glipizide_metformin_Steady
float64
glimepiride_pioglitazone_Steady
float64
metformin_rosiglitazone_Steady
float64
metformin_pioglitazone_Steady
float64
change_No
float64
diabetesMed_Yes
float64
age_target_encoded
float64
payer_code_target_encoded
float64
medical_specialty_target_encoded
float64
diag_1_target_encoded
float64
diag_2_target_encoded
float64
diag_3_target_encoded
float64
diag_1_cat_target_encoded
float64
diag_2_cat_target_encoded
float64
diag_3_cat_target_encoded
float64
target
int64
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Check out the documentation for more information.

Hospital Readmission Risk - Preprocessed Dataset

Dataset Description

This dataset contains preprocessed hospital readmission data for diabetic patients. The goal is to predict 30-day hospital readmissions to enable proactive interventions.

Dataset Summary

  • Total samples: 101,766
  • Features: 113
  • Target: Binary (0=No readmission, 1=Readmission within 30 days)
  • Class distribution: No readmission: 90,409 (88.8%), Readmission: 11,357 (11.2%)

Data Splits

  • Training: 71,236 samples
  • Validation: 15,265 samples
  • Test: 15,265 samples

Preprocessing Applied

  1. Missing Value Handling: Median/mode imputation with group-wise strategies
  2. Data Validation: Value ranges, data types, and domain constraints checked
  3. Outlier Treatment: IQR-based winsorization
  4. Feature Engineering:
    • Diagnosis code aggregation into clinical categories
    • Utilization features (group-by statistics)
    • Medication complexity scores
    • Age/BMI categorical buckets
    • Interaction features
  5. Encoding: One-hot (low-cardinality) + CV-safe target encoding (high-cardinality)
  6. Normalization: StandardScaler applied

Usage

import pandas as pd

# Load full dataset
data = pd.read_csv('hospital_readmission_full.csv')
X = data.drop('target', axis=1)
y = data['target']

# Or load splits
train = pd.read_csv('splits/train.csv')
val = pd.read_csv('splits/validation.csv')
test = pd.read_csv('splits/test.csv')

Citation

Original Dataset: Diabetes 130-US Hospitals for Years 1999-2008
UCI Machine Learning Repository
https://archive.ics.uci.edu/dataset/296/diabetes-130-us-hospitals-for-years-1999-2008

License

See original dataset license from UCI Machine Learning Repository.

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