Datasets:

ClarusC64
/

clinical-immune-dysregulation-cascade-v0.1

scenario_id string	crp_t0 int64	crp_t1 int64	crp_t2 int64	il6_proxy_t0 int64	il6_proxy_t1 int64	il6_proxy_t2 int64	temp_t0 float64	temp_t1 float64	temp_t2 float64	wbc_t0 float64	wbc_t1 float64	wbc_t2 float64	immune_regulation_proxy float64	organ_stress_proxy float64	treatment_delay int64	lab_noise float64	chart_noise float64	label int64
IM001	48	52	55	18	20	21	37.8	37.9	38	11.8	12	12.1	0.74	0.32	1	0.31	0.4	0
IM002	50	68	95	20	34	58	37.9	38.4	39	12	14.5	17.2	0.36	0.68	4	0.33	0.42	1
IM003	42	45	47	16	17	18	37.6	37.7	37.8	10.8	11	11.1	0.8	0.28	1	0.28	0.36	0
IM004	46	64	92	18	32	55	37.8	38.3	38.9	11.6	14	16.8	0.34	0.7	4	0.35	0.43	1
IM005	52	54	56	21	22	23	38	38.1	38.1	12.3	12.4	12.5	0.72	0.35	1	0.3	0.38	0
IM006	55	76	108	23	40	66	38.1	38.6	39.2	12.8	15.4	18	0.33	0.73	4	0.37	0.44	1
IM007	40	42	44	15	16	17	37.5	37.6	37.6	10.5	10.7	10.8	0.82	0.26	1	0.27	0.35	0
IM008	49	70	98	19	35	60	37.9	38.5	39.1	11.9	14.8	17.5	0.35	0.69	3	0.34	0.41	1
IM009	47	50	53	18	19	20	37.7	37.8	37.9	11.4	11.6	11.8	0.76	0.31	1	0.29	0.37	0
IM010	53	74	104	22	38	64	38	38.6	39.2	12.4	15.2	17.9	0.34	0.72	4	0.36	0.42	1
IM011	43	46	48	16	17	18	37.6	37.7	37.8	10.9	11.1	11.2	0.79	0.29	1	0.28	0.36	0
IM012	58	80	112	24	42	70	38.2	38.8	39.4	13	15.8	18.6	0.32	0.75	4	0.37	0.44	1
IM013	51	54	56	20	21	22	38	38	38.1	12.1	12.3	12.4	0.73	0.34	1	0.3	0.38	0
IM014	48	67	96	19	34	59	37.8	38.4	39	11.8	14.6	17.3	0.35	0.69	3	0.34	0.41	1
IM015	41	43	45	15	16	17	37.5	37.6	37.7	10.6	10.8	10.9	0.83	0.27	1	0.27	0.35	0
IM016	48	52	55	18	20	21	37.8	37.9	38	11.8	12	12.1	0.74	0.32	1	0.31	0.4	0
IM017	48	52	55	18	26	42	37.8	38.1	38.5	11.8	13.4	15.3	0.42	0.58	3	0.31	0.4	1
IM018	42	45	47	16	17	18	37.6	37.7	37.8	10.8	11	11.1	0.8	0.28	1	0.28	0.36	0
IM019	42	45	47	16	25	40	37.6	38	38.4	10.8	13	15	0.43	0.57	4	0.28	0.36	1
IM020	52	54	56	21	22	23	38	38.1	38.1	12.3	12.4	12.5	0.72	0.35	1	0.3	0.38	0
IM021	55	76	108	23	40	66	38.1	38.6	39.2	12.8	15.4	18	0.33	0.73	4	0.37	0.44	1
IM022	40	42	44	15	16	17	37.5	37.6	37.6	10.5	10.7	10.8	0.82	0.26	1	0.27	0.35	0
IM023	49	70	98	19	35	60	37.9	38.5	39.1	11.9	14.8	17.5	0.35	0.69	3	0.34	0.41	1
IM024	47	50	53	18	19	20	37.7	37.8	37.9	11.4	11.6	11.8	0.76	0.31	1	0.29	0.37	0
IM025	53	74	104	22	38	64	38	38.6	39.2	12.4	15.2	17.9	0.34	0.72	4	0.36	0.42	1
IM026	43	46	48	16	17	18	37.6	37.7	37.8	10.9	11.1	11.2	0.79	0.29	1	0.28	0.36	0
IM027	58	80	112	24	42	70	38.2	38.8	39.4	13	15.8	18.6	0.32	0.75	4	0.37	0.44	1
IM028	51	54	56	20	21	22	38	38	38.1	12.1	12.3	12.4	0.73	0.34	1	0.3	0.38	0
IM029	48	67	96	19	34	59	37.8	38.4	39	11.8	14.6	17.3	0.35	0.69	3	0.34	0.41	1
IM030	41	43	45	15	16	17	37.5	37.6	37.7	10.6	10.8	10.9	0.83	0.27	1	0.27	0.35	0

clinical-immune-dysregulation-cascade-v0.1

What this dataset does

This dataset evaluates whether models can detect immune dysregulation before overt systemic deterioration.

Each row represents a short inflammatory trajectory across three time points.

The task is to classify whether the immune response remains regulated or is moving toward inflammatory cascade instability.

Core stability idea

Immune instability does not depend on one inflammatory marker alone.

A patient may show elevated markers but remain stable if regulation and treatment timing remain adequate.

Conversely, moderate inflammatory escalation can become unstable when cytokine activity, temperature trend, leukocyte response, organ stress, and delayed treatment reinforce each other.

The dataset tests interaction reasoning across:

CRP trajectory
IL-6 proxy trajectory
temperature trajectory
WBC trajectory
immune regulation proxy
organ stress proxy
treatment delay

Prediction target

label = 1 → immune dysregulation cascade risk
label = 0 → regulated or stable inflammatory trajectory

Row structure

Each row includes:

CRP trajectory
IL-6 proxy trajectory
temperature trajectory
WBC trajectory
immune regulation proxy
organ stress proxy
treatment delay

Decoy variables:

lab_noise
chart_noise

These variables appear meaningful but do not determine the label alone.

Evaluation

Predictions must use: scenario_id,prediction IM101,0 IM102,1

Run:

python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json

Metrics returned:

accuracy
precision
recall
f1
confusion matrix
dataset integrity diagnostics

Structural Note

This dataset reflects latent stability geometry through observable proxies.

The generator and latent rule structure are not included.

This dataset is part of the Clarus Stability Reasoning Benchmark.

Production Deployment

This dataset is intended as a compact benchmark for immune-regulation and inflammatory-cascade reasoning.

It is not a clinical decision tool.

Enterprise & Research Collaboration

This dataset supports research into:

immune dysregulation detection
inflammatory cascade dynamics
trajectory-based reasoning
latent stability geometry
cross-domain instability benchmarks

License

MIT

Downloads last month: 12

Number of rows:

Total file size:

17.8 kB