Datasets:

ClarusC64
/

clinical-escalation-discipline-v0.1

scenario_id string	news_score int64	heart_rate int64	resp_rate int64	map int64	lactate_trend string	oxygen_requirement int64	urine_output_trend string	treatment_response string	mental_status_change string	label int64
train_001	2	84	18	78	stable	0	stable	improving	none	0
train_002	3	88	20	76	falling	0	stable	improving	none	0
train_003	3	91	21	75	stable	1	improving	partial	none	0
train_004	4	96	22	74	falling	1	stable	improving	none	0
train_005	4	94	22	76	stable	1	stable	partial	none	0
train_006	5	102	24	72	rising	1	worsening	poor	mild	1
train_007	6	108	25	70	rising	2	worsening	poor	mild	1
train_008	7	112	27	68	rising	2	worsening	none	moderate	1
train_009	5	99	24	73	rising	1	stable	poor	mild	1
train_010	6	105	26	71	stable	2	worsening	poor	mild	1
train_011	4	92	22	75	rising	0	stable	partial	none	0
train_012	5	98	23	74	falling	1	stable	improving	none	0
train_013	5	100	24	72	rising	1	improving	partial	none	0
train_014	6	104	25	70	rising	1	worsening	poor	mild	1
train_015	6	106	26	69	rising	2	stable	poor	mild	1
train_016	7	114	28	67	rising	2	worsening	none	moderate	1
train_017	3	87	20	77	stable	0	stable	partial	none	0
train_018	4	95	22	75	falling	1	stable	improving	none	0
train_019	6	109	26	70	rising	2	worsening	poor	moderate	1
train_020	7	116	29	66	rising	2	worsening	none	moderate	1

What this dataset does

This dataset tests whether a model can decide when a patient should be escalated rather than simply monitored.

The task is not to identify the sickest patient by a single score.

The task is to decide whether the current pattern requires escalation.

Core stability idea

Escalation depends on more than visible severity.

A patient with a moderate score may need escalation if the trajectory is worsening and treatment response is poor.

A patient with a similar score may be safe to monitor if treatment response is improving and buffer signals are stable.

Prediction target

The label column is binary.

Label 1 means escalate.

Label 0 means monitor.

Row structure

Each row contains:

scenario_id
news_score
heart_rate
resp_rate
map
lactate_trend
oxygen_requirement
urine_output_trend
treatment_response
mental_status_change
label

treatment_response uses:

improving
partial
poor
none

oxygen_requirement uses:

0 = room air or minimal support
1 = low-flow oxygen
2 = high oxygen requirement

mental_status_change uses:

none
mild
moderate

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
Precision
Recall
F1
Confusion matrix
Structural Note

This dataset tests escalation discipline under uncertainty.

It is designed to prevent a simple rule such as "high NEWS score equals escalate" or "normal MAP equals monitor."

The intended reasoning requires attention to trajectory, treatment response, reserve capacity, and emerging instability.

The dataset does not expose the hidden rationale behind each label.

License

MIT

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Number of rows:

Total file size:

9.52 kB