Healthcare
16 columns
200 rows
8 formats

Patient Risk Score Validation

This dataset provides comprehensive patient-level risk score assessments generated by AI models, including demographic, clinical, and provider information. It enables benchmarking of risk stratification models, supports early intervention tracking, and facilitates resource allocation analysis for healthcare providers. Ideal for validating predictive models and optimizing patient care strategies.

last updated 20 Aug 2025
by GoMask

The brief that made it

Benchmarking and validation of AI-driven risk scoring models

Sample rows

preview · 8 of 200 rows · all 16 columns

patient_idstring	outcomestring	ageinteger	intervention_flagboolean	comorbiditiesstring	risk_scorefloat	risk_score_modelstring	genderstring	ethnicitystring	diagnosis_codestring	admission_datedate	discharge_datedate	provider_idstring	provider_namestring	resource_allocation_levelstring	benchmark_groupstring
P0001	mortality	112	true	E11.9,I10	0.92	RS-v2.0	male	White	I50.9	2022-03-16	2022-03-29	HOSP101A	St. Gabriel General Hospital	critical	cardiac
P0002	stable	27	false	blank	0.16	RS-v1.2	female	Hispanic/Latino	F32.0	2024-01-11	2024-01-21	HOSP104B	Crescent Community Health Center	standard	behavioral
P0003	complication	58	true	E78.5	0.85	RS-v3.1	male	Black	C34.10	2023-09-19	2023-10-10	HOSP110C	Valley Oncology Center	enhanced	oncology
P0004	mortality	120	true	E11.9,I10	0.99	RS-v2.0	unknown	Asian	N18.5	2021-11-02	2021-11-25	HOSP108D	Riverbend Senior Care Hospital	critical	renal
P0005	complication	8	false	blank	0.42	RS-v1.0	female	Native American	J21.9	2024-03-05	2024-03-09	HOSP105E	Sunrise Children's Hospital	standard	pediatric
P0006	stable	43	false	blank	0.07	RS-v1.2	male	White	E66.9	2023-05-13	2023-05-20	HOSP103F	Harmony Wellness Center	standard	general
P0007	stable	37	false	E11.9	0.65	RS-v3.1	female	South Asian	I10	2023-10-22	2023-11-02	HOSP102G	Lakeside Family Practice	standard	hypertension
P0008	readmission	24	false	blank	0.21	RS-v2.0	other	Middle Eastern	F41.1	2022-07-14	2022-07-21	HOSP106H	Summit Mental Health Center	standard	behavioral
P0009	complication	78	true	E11.9,I10	0.81	RS-v1.2	male	East Asian	I63.9	2021-08-05	2021-08-22	HOSP101A	St. Gabriel General Hospital	critical	cardiac
P0010	stable	52	false	E11.9	0.33	RS-v3.1	female	Black	E14	2024-02-17	2024-02-25	HOSP107J	Mercy Diabetes Center	standard	diabetes
P0011	readmission	49	false	E78.5	0.56	RS-v1.0	male	White	J20.9	2023-07-08	2023-07-18	HOSP109K	Pinecrest Respiratory Clinic	enhanced	respiratory
P0012	unknown	38	false	blank	0.14	RS-v2.0	female	White	F32.1	2022-09-01	2022-09-10	HOSP104B	Crescent Community Health Center	standard	behavioral
P0013	complication	66	true	E11.9,I10	0.73	RS-v1.2	male	Asian	I10	2023-12-21	2023-12-31	HOSP102G	Lakeside Family Practice	critical	hypertension
P0014	complication	2	true	blank	0.95	RS-v3.1	other	Native Hawaiian	P07.3	2023-05-01	2023-05-12	HOSP105E	Sunrise Children's Hospital	critical	neonatal
P0015	stable	41	false	blank	0.12	RS-v1.0	female	White	E66.9	2024-03-14	2024-03-21	HOSP103F	Harmony Wellness Center	standard	general
P0016	stable	55	false	I10	0.58	RS-v2.0	male	Pacific Islander	E11.9	2022-06-19	2022-07-05	HOSP107J	Mercy Diabetes Center	standard	diabetes
P0017	mortality	99	true	E11.9,I10	0.87	RS-v1.2	male	White	I50.9	2023-01-26	2023-02-10	HOSP101A	St. Gabriel General Hospital	critical	cardiac
P0018	readmission	23	false	blank	0.34	RS-v3.1	female	Black	F32.0	2022-02-09	2022-02-17	HOSP104B	Crescent Community Health Center	standard	behavioral
P0019	stable	45	false	blank	0.19	RS-v2.0	female	White	E66.9	2024-04-02	2024-04-09	HOSP103F	Harmony Wellness Center	standard	general
P0020	complication	87	true	E11.9,I10	0.78	RS-v1.0	unknown	East Asian	N18.5	2023-02-17	2023-03-10	HOSP108D	Riverbend Senior Care Hospital	critical	renal

What the 200 rows show

from the 200-row sample

Mortality outcome stands out: mean age is 106.8, against 41.2 for the rest.

35%intervention_flag = true
45.5median age
3resource allocation levels
4risk score models
4genders
10benchmark groups

Mean age by outcome200 rows

age200 rows, in bands of 20

Median 45.5, from 0 to 120.

comorbidities96 rows with a value · 104 left blank

E11.9,I1030
I1022
E11.920
E78.56
D64.95
I10,E11.94
I10,E142
I10,F41.11
E11.9,I10,N18.51
I10,E11.9,N18.51

16 columns by typefrom the column list below

string 11
integer 1
float 1
date 2
boolean 1

Columns

16 columns in four groups

blueprint · 16 columns
column	type	description	example
Text 11 columns
`patient_id`	string	Unique identifier for each patient in the datasetunique	P0001
`risk_score_model`	string	Name or version of the risk scoring model used to generate the risk score4 models	RS-v2.0
`gender`	string	Patient's gendermale · female · other · unknown	male
`ethnicity`	string	Patient's self-reported ethnicity12 ethnicities · optional	White
`diagnosis_code`	string	Primary diagnosis code (ICD-10 or similar) relevant to risk assessment	I50.9
`comorbidities`	string	Comma-separated list of comorbid conditions (ICD-10 codes or standard names)optional	E11.9,I10
`outcome`	string	Observed patient outcome (e.g., 'readmission', 'mortality', 'stable', 'complication')readmission · mortality · stable · complication · unknown · optional	mortality
`provider_id`	string	Unique identifier for the healthcare provider or facility	HOSP101A
`provider_name`	string	Name of the healthcare provider or facilityoptional	Valley Oncology Center
`resource_allocation_level`	string	Level of resources allocated to the patient (e.g., 'standard', 'enhanced', 'critical')standard · enhanced · critical · unknown · optional	critical
`benchmark_group`	string	Benchmark group or cohort for model comparison (e.g., 'diabetes', 'cardiac', 'general')10 groups · optional	cardiac
Numbers 2 columns
`risk_score`	float	AI-generated risk score indicating the patient's likelihood of adverse health outcomes0 to 1	0.92
`age`	integer	Patient's age in years at the time of risk assessment0 to 120	112
Dates and times 2 columns
`admission_date`	date	Date of patient admission or risk assessment	2022-03-16
`discharge_date`	date	Date of patient discharge (if applicable)optional	2022-03-29
True or false 1 column
`intervention_flag`	boolean	Indicates whether an early intervention was initiated based on risk scoreoptional	true

Use it for

A healthcare dashboard
The intervention_flag rate, age by outcome and a breakdown of comorbidities. Excel, Power BI or Tableau.
Why do the 20 mortality rows have a mean age of 106.8?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Patients200
P0001112mortality
P000227stable
P000358complica…
A software demo
Believable patients with risk_score, risk_score_model and age to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columns

blueprint · patient-risk-score-validation

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Includes demographic, clinical, and lifestyle variables
Binary outcome for adverse event occurrence
Risk scores generated by different algorithms
HIPAA-compliant simulated data

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: patient-risk-score-validation

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?