Datasets:
scenario_id stringlengths 9 9 | oxygen_requirement int64 0 3 | vasopressor_requirement stringclasses 4
values | renal_function stringclasses 4
values | urine_output stringclasses 4
values | mental_status stringclasses 3
values | respiratory_reserve stringclasses 4
values | circulatory_reserve stringclasses 4
values | renal_reserve stringclasses 4
values | label int64 0 2 |
|---|---|---|---|---|---|---|---|---|---|
train_001 | 0 | 0 | normal | normal | baseline | high | high | high | 0 |
train_002 | 0 | 0 | mild_impairment | normal | baseline | high | high | medium | 0 |
train_003 | 1 | 0 | normal | normal | baseline | medium | high | high | 0 |
train_004 | 0 | low | mild_impairment | normal | baseline | high | medium | medium | 0 |
train_005 | 1 | low | mild_impairment | reduced | baseline | medium | medium | medium | 1 |
train_006 | 1 | low | moderate_impairment | reduced | mild_confusion | medium | medium | low | 1 |
train_007 | 2 | low | moderate_impairment | reduced | baseline | low | medium | low | 1 |
train_008 | 1 | moderate | moderate_impairment | oliguria | mild_confusion | medium | low | low | 1 |
train_009 | 2 | moderate | moderate_impairment | oliguria | mild_confusion | low | low | low | 2 |
train_010 | 2 | high | severe_impairment | oliguria | confused | low | low | very_low | 2 |
train_011 | 3 | moderate | severe_impairment | oliguria | confused | very_low | low | very_low | 2 |
train_012 | 3 | high | severe_impairment | anuria | confused | very_low | very_low | very_low | 2 |
train_013 | 0 | 0 | normal | normal | baseline | high | high | high | 0 |
train_014 | 1 | 0 | mild_impairment | normal | baseline | medium | high | medium | 0 |
train_015 | 1 | low | moderate_impairment | reduced | baseline | medium | medium | low | 1 |
train_016 | 2 | low | moderate_impairment | reduced | mild_confusion | low | medium | low | 1 |
train_017 | 2 | moderate | severe_impairment | oliguria | mild_confusion | low | low | very_low | 2 |
train_018 | 3 | moderate | severe_impairment | oliguria | confused | very_low | low | very_low | 2 |
train_019 | 3 | high | severe_impairment | anuria | confused | very_low | very_low | very_low | 2 |
train_020 | 0 | low | normal | normal | baseline | high | medium | high | 0 |
What this dataset does
This dataset tests whether a model can estimate how close a patient is to a treatment boundary.
The task is not to predict diagnosis.
The task is to classify pressure on the patient system.
Core stability idea
A patient may be stable at the present moment while operating close to a boundary.
Constraint pressure increases when support needs rise and reserve capacity falls.
The model must classify whether pressure is low, medium, or high.
Prediction target
The label column has three classes.
Label 0 means low constraint pressure.
Label 1 means medium constraint pressure.
Label 2 means high constraint pressure.
Row structure
Each row contains:
- scenario_id
- oxygen_requirement
- vasopressor_requirement
- renal_function
- urine_output
- mental_status
- respiratory_reserve
- circulatory_reserve
- renal_reserve
- label
oxygen_requirement uses:
- 0 = room air or minimal support
- 1 = low oxygen requirement
- 2 = high oxygen requirement
- 3 = near respiratory boundary
vasopressor_requirement uses:
- 0 = none
- low
- moderate
- high
reserve fields use:
- high
- medium
- low
- very_low
Evaluation
Submissions must contain:
scenario_id,prediction
test_001,0
test_002,0
test_003,1
Run:
python scorer.py predictions.csv
Optional truth path:
python scorer.py predictions.csv data/test.csv
The scorer reports:
Accuracy
Macro precision
Macro recall
Macro F1
Confusion matrix
Structural Note
This dataset tests boundary awareness.
It is designed to prevent shortcut logic based only on current vital signs.
The intended reasoning requires integration across support requirements, organ reserve, and proximity to treatment limits.
The dataset does not expose the hidden rationale behind each label.
License
MIT
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