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license: cc-by-4.0
task_categories:
- text-classification
dataset_version: 0.2.3
dataset_info:
features:
- name: FEATURE_phases
list:
class_label:
names:
'0': NA
'1': EARLY_PHASE1
'2': PHASE1
'3': PHASE2
'4': PHASE3
'5': PHASE4
- name: FEATURE_enrollmentCount
dtype: int64
- name: FEATURE_allocation
dtype: string
- name: FEATURE_interventionModel
dtype: string
- name: FEATURE_primaryPurpose
dtype:
class_label:
names:
'0': TREATMENT
'1': PREVENTION
'2': DIAGNOSTIC
'3': ECT
'4': SUPPORTIVE_CARE
'5': SCREENING
'6': HEALTH_SERVICES_RESEARCH
'7': BASIC_SCIENCE
'8': DEVICE_FEASIBILITY
'9': OTHER
- name: FEATURE_masking
dtype:
class_label:
names:
'0': NONE
'1': SINGLE
'2': DOUBLE
'3': TRIPLE
'4': QUADRUPLE
- name: FEATURE_healthyVolunteers
dtype: bool
- name: FEATURE_sex
dtype:
class_label:
names:
'0': ALL
'1': FEMALE
'2': MALE
- name: FEATURE_oversightHasDmc
dtype: bool
- name: FEATURE_briefSummary
dtype: string
- name: FEATURE_detailedDescription
dtype: string
- name: FEATURE_conditions
dtype: string
- name: FEATURE_conditionsKeywords
dtype: string
- name: FEATURE_protocolPdfText
dtype: string
- name: FEATURE_numArms
dtype: int64
- name: FEATURE_armDescriptions
dtype: string
- name: FEATURE_armGroupTypes
list:
class_label:
names:
'0': EXPERIMENTAL
'1': ACTIVE_COMPARATOR
'2': PLACEBO_COMPARATOR
'3': SHAM_COMPARATOR
'4': NO_INTERVENTION
'5': OTHER
- name: FEATURE_numInterventions
dtype: int64
- name: FEATURE_interventionTypes
list:
class_label:
names:
'0': DRUG
'1': DEVICE
'2': BIOLOGICAL
'3': PROCEDURE
'4': RADIATION
'5': BEHAVIORAL
'6': GENETIC
'7': DIETARY_SUPPLEMENT
'8': COMBINATION_PRODUCT
'9': DIAGNOSTIC_TEST
'10': OTHER
- name: FEATURE_interventionDescriptions
dtype: string
- name: FEATURE_interventionNames
dtype: string
- name: FEATURE_numLocations
dtype: int64
- name: FEATURE_locationDetails
dtype: string
- name: LABEL_ct_level_ade_population
dtype: int64
- name: LABEL_sum_dosing_errors
dtype: int64
- name: LABEL_dosing_error_rate
dtype: float32
- name: LABEL_wilson_label
dtype: int64
- name: METADATA_nctId
dtype: string
- name: METADATA_overallStatus
dtype:
class_label:
names:
'0': ACTIVE_NOT_RECRUITING
'1': COMPLETED
'2': ENROLLING_BY_INVITATION
'3': NOT_YET_RECRUITING
'4': RECRUITING
'5': SUSPENDED
'6': TERMINATED
'7': WITHDRAWN
'8': AVAILABLE
'9': NO_LONGER_AVAILABLE
'10': TEMPORARILY_NOT_AVAILABLE
'11': APPROVED_FOR_MARKETING
'12': WITHHELD
'13': UNKNOWN
- name: METADATA_completionDate
dtype: date32
- name: METADATA_startDate
dtype: date32
- name: METADATA_leadSponsorName
dtype: string
- name: METADATA_leadSponsorClass
dtype:
class_label:
names:
'0': NIH
'1': FED
'2': OTHER_GOV
'3': INDIV
'4': INDUSTRY
'5': NETWORK
'6': AMBIG
'7': OTHER
'8': UNKNOWN
- name: METADATA_hasProtocol
dtype: bool
- name: METADATA_hasSap
dtype: bool
- name: METADATA_hasIcf
dtype: bool
- name: METADATA_protocolPdfLinks
dtype: string
- name: METADATA_count_Accidental drug intake by child
dtype: int64
- name: METADATA_count_Accidental overdose
dtype: int64
- name: METADATA_count_Accidental overdose (therapeutic agent)
dtype: int64
- name: METADATA_count_Accidental underdose
dtype: int64
- name: METADATA_count_Deliberate overdose
dtype: int64
- name: METADATA_count_Dose calculation error
dtype: int64
- name: METADATA_count_Drug administration error
dtype: int64
- name: METADATA_count_Drug overdose
dtype: int64
- name: METADATA_count_Drug overdose accidental
dtype: int64
- name: METADATA_count_Extra dose administered
dtype: int64
- name: METADATA_count_Incorrect dosage administered
dtype: int64
- name: METADATA_count_Incorrect dose administered
dtype: int64
- name: METADATA_count_Incorrect drug administration duration
dtype: int64
- name: METADATA_count_Incorrect drug administration rate
dtype: int64
- name: METADATA_count_Incorrect product administration duration
dtype: int64
- name: METADATA_count_Intentional overdose
dtype: int64
- name: METADATA_count_Medication error
dtype: int64
- name: METADATA_count_Medication monitoring error
dtype: int64
- name: METADATA_count_Multiple drug overdose
dtype: int64
- name: METADATA_count_Multiple drug overdose accidental
dtype: int64
- name: METADATA_count_Multiple drug overdose intentional
dtype: int64
- name: METADATA_count_Multiple use of single-use product
dtype: int64
- name: METADATA_count_Non-accidental overdose
dtype: int64
- name: METADATA_count_Overdose
dtype: int64
- name: METADATA_count_Overdose NOS
dtype: int64
- name: METADATA_count_Overmedication
dtype: int64
- name: METADATA_count_Prescribed overdose
dtype: int64
- name: METADATA_count_Treatment noncompliance
dtype: int64
- name: METADATA_count_Underdose
dtype: int64
- name: METADATA_count_Unintentional medical device removal
dtype: int64
- name: METADATA_count_Unintentional medical device removal by patient
dtype: int64
- name: METADATA_wilson_lower_bound
dtype: float32
splits:
- name: train
num_bytes: 1263717270
num_examples: 29478
- name: validation
num_bytes: 1381971767
num_examples: 6316
- name: test
num_bytes: 1534015699
num_examples: 6318
download_size: 1829605503
dataset_size: 4179704736
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for ct-dosing-errors
This dataset provides the materials accompanying the paper "[Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning](https://huggingface.co/papers/2602.22285)".
The dataset is designed for the prediction of dosing errors in interventional clinical research. It comprises 42,112 clinical trials extracted from ClinicalTrials.gov, containing structured, semi-structured trial data, and unstructured protocol-related free-text data.
## Links
- **Paper:** [https://huggingface.co/papers/2602.22285](https://huggingface.co/papers/2602.22285)
- **GitHub Repository:** [https://github.com/ds4dh/CT-dosing-errors](https://github.com/ds4dh/CT-dosing-errors)
## Sample Usage
You can load the dataset using the `datasets` library:
```python
from datasets import load_dataset
ds = load_dataset(
"ds4dh/ct-dosing-errors",
split="train"
)
print(ds)
print(ds.features)
```
## Dataset Summary
The dataset includes labels for the prediction of dosing errors derived from adverse event reports and MedDRA terminology. It features a wide range of fields including:
- **Textual data:** Brief summaries, detailed descriptions, conditions, and protocol PDF text.
- **Structured data:** Clinical trial phases, enrollment counts, allocation, and intervention types.
- **Labels:** Binary indicators (`LABEL_wilson_label`) for elevated dosing error rates.
## Version
This repository contains dataset version **0.2.3**.
## License
This dataset is licensed under **CC BY 4.0** (`cc-by-4.0`). |