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Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
guideline_id: int64
filename: string
language: string
text: string
paths: null
primary_clinical_focus: string
to
{'guideline_id': Value('int64'), 'filename': Value('string'), 'language': Value('string'), 'primary_clinical_focus': Value('string'), 'paths': List(List(Json(decode=True)))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
guideline_id: int64
filename: string
language: string
text: string
paths: null
primary_clinical_focus: string
to
{'guideline_id': Value('int64'), 'filename': Value('string'), 'language': Value('string'), 'primary_clinical_focus': Value('string'), 'paths': List(List(Json(decode=True)))}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
guideline_id int64 | filename string | language string | primary_clinical_focus string | paths list |
|---|---|---|---|---|
1 | 1 | en | screening | [
[
{
"index": 1,
"condition": "Adult patient (age ≥ 40 years)?",
"decision_outcome": "yes"
},
{
"index": 2,
"condition": "Chronic cognitive slowing reported by patient or informant for ≥ 6 months?",
"decision_outcome": "yes"
},
{
"index": 3,
"condit... |
2 | 2 | en | management | [
[
{
"index": 1,
"condition": "Does the patient have acute joint red flags (fever ≥38.0°C, hemodynamic instability, rapidly progressive severe pain, or inability to bear weight/use the joint)?",
"decision_outcome": "yes"
},
{
"index": 2,
"condition": "Is there clinical suspi... |
3 | 3 | en | classification | [
[
{
"index": 1,
"condition": "Does the patient have recurrent episodic postprandial epigastric or upper abdominal pain consistent with pancreatic ductal outflow obstruction (e.g., within 1–3 hours after meals, lasting minutes to hours, recurring over weeks)?",
"decision_outcome": "yes"
},
... |
4 | 4 | en | management | [
[
{
"index": 1,
"condition": "Is the patient currently hemodynamically unstable (e.g., sustained systolic BP <90 mmHg, signs of shock) attributable to pulmonary bleeding/respiratory failure?",
"decision_outcome": "yes"
},
{
"index": 2,
"condition": "Is there evidence of lif... |
5 | 5 | en | classification | [[{"index":1,"condition":"Does the patient have objective evidence of autonomic failure (e.g., abnor(...TRUNCATED) |
6 | 6 | en | classification | [[{"index":1,"condition":"Age at symptom onset is <18 years","decision_outcome":"yes"},{"index":2,"c(...TRUNCATED) |
7 | 7 | en | diagnosis | [[{"index":1,"condition":"Patient has polyuria (24-hour urine volume >3 L/day in adults, or >40 mL/k(...TRUNCATED) |
8 | 8 | en | diagnosis | [[{"index":1,"condition":"Patient has exertional dyspnea and/or exercise intolerance persisting for (...TRUNCATED) |
9 | 9 | en | screening | [[{"index":1,"condition":"Patient is a first-degree relative of an individual with confirmed Familia(...TRUNCATED) |
10 | 10 | en | diagnosis | [[{"index":1,"condition":"Does the patient have a localized, persistent swelling of skin/soft tissue(...TRUNCATED) |
SAGA-CDP
This dataset consists of synthetic guideline-adherence data generated by the SAGA-CDP pipeline for training large language models (LLMs) to follow guideline-defined clinical decision pathways (CDPs).
Overview
The dataset is constructed from fictional clinical concepts and is designed to provide synthetic training data for guideline-adherent clinical decision-making.
It contains 2,000 fictional clinical guidelines and 76,584 corresponding clinical cases with explicit reference clinical decision pathways (CDPs).
The data are initialized from 1,000 fictional disease concepts across five categories:
- Fantasy
- Science Fiction
- Xuanhuan
- Famous Fictional Works
- Realistic Medical Variants
Each disease concept is instantiated in English and Chinese, yielding 2,000 disease entries in total.
Each instance includes:
- A fictional clinical guideline
- A clinical case vignette
- A reference clinical decision pathway (CDP)
- A final clinical decision outcome
Usage
This dataset is intended primarily for research on guideline-adherence training.
In our experiments, the synthetic data are used to train large language models to follow guideline-defined clinical decision pathways and to study whether the learned capability transfers to unseen real-world clinical guidelines.
The synthetic data can also be combined with real-world guideline-derived data as an augmentation source.
For the corresponding real-world guideline dataset and benchmark, see MEGA-CDP.
Funding
Funding: Shanghai General AI Foundation Models Program (Grant No. 2025SHZDZX025G10)
Technical Support: Shanghai Artificial Intelligence Laboratory
License
TBD. Intended for academic and research use.
Citation
TBD.
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