Healthcare
21 columns
200 rows
8 formats

Patient Health Risk Factor Scores

This dataset provides detailed records of patient health risk assessments, including demographic data, clinical measurements, and calculated risk factor scores for chronic disease prediction. It is ideal for population health analytics, risk stratification, and supporting proactive care management in healthcare settings.

last updated 12 Jul 2025
by GoMask

The brief that made it

Population health risk stratification and predictive analytics

Sample rows

preview · 8 of 200 rows · all 21 columns

assessment_idstring	risk_categorystring	risk_factor_scorefloat	family_history_chronic_diseaseboolean	ethnicitystring	patient_idstring	assessment_datedate	ageinteger	sexstring	smoking_statusstring	bmifloat	systolic_bpinteger	diastolic_bpinteger	cholesterol_totalfloat	hdl_cholesterolfloat	ldl_cholesterolfloat	diabetes_statusstring	hypertension_statusstring	physical_activity_levelstring	assessor_idstring	notesstring
A00001	low	7.4	false	White	P1001	2023-02-15	27	female	never	21.5	112	74	146.2	62.8	84.9	no	no	high	CLN001	Routine assessment, no risk factors detected.
A00002	high	47.2	true	Black	P1002	2022-10-06	53	male	former	28.7	138	86	211.5	43.1	135.7	no	yes	low	CLN002	Advised lifestyle modification and regular follow-up.
A00003	low	17.8	false	Asian	P1003	2023-06-19	43	female	never	25.3	124	78	167.4	54.2	93.6	no	no	moderate	CLN003	Patient encouraged to maintain current lifestyle.
A00004	very high	92.6	true	Hispanic	P1004	2023-01-13	66	male	current	32.2	152	98	274.3	27.5	170.1	yes	yes	low	CLN004	Multiple comorbidities. Immediate intervention recommended.
A00005	low	4.6	false	Other	P1005	2022-12-22	18	female	never	20.1	112	73	134.8	52.6	63.4	no	no	high	CLN005	Pediatric assessment, healthy young adult.
A00006	low	0.2	false	blank	P1006	2023-03-27	0	unknown	unknown	10	60	30	50	10	10	unknown	unknown	unknown	CLN999	Boundary age, neonatal record.
A00007	moderate	34.5	true	White	P1007	2023-04-10	45	female	former	29.4	139	84	205.6	40.9	130.5	no	no	moderate	CLN002	Family history noted, advised annual review.
A00008	very high	99.5	true	Asian	P1008	2023-05-05	61	male	current	36.3	168	109	342.1	22.1	220.8	yes	yes	low	CLN004	Critical risk. Referred to specialist.
A00009	low	13.1	false	White	P1009	2022-09-28	36	other	never	24.2	117	76	159.8	53.7	81.4	no	no	high	CLN006	Healthy, well-controlled metrics.
A00010	very high	75.3	true	Hispanic	P1010	2023-02-28	58	male	former	31.7	140	85	227.4	37.2	145.1	yes	yes	low	CLN003	Risk escalation, increase in BP and BMI since last year.
A00011	low	4.9	false	Asian	P1011	2023-03-13	22	male	never	19.4	114	67	138.2	64.1	59.2	no	no	high	CLN007	No risk factors, advised annual check.
A00012	low	9.2	false	White	P1001	2024-01-18	28	female	never	22	115	73	149.7	63.2	71.8	no	no	high	CLN001	Continues to maintain healthy lifestyle.
A00013	moderate	24.1	true	Black	P1012	2023-07-20	41	female	never	26.8	128	82	178.4	48.3	101.6	no	no	moderate	CLN008	Some family history, no current disease.
A00014	very high	88.1	true	Other	P1013	2023-05-22	59	male	current	34.4	145	92	289.8	19.9	198.7	yes	yes	low	CLN003	Immediate intervention required.
A00015	low	11.8	false	White	P1014	2023-02-09	38	female	never	22.6	118	76	153.7	57.6	75.8	no	no	high	CLN006	Low risk, routine assessment.
A00016	moderate	27.9	true	Asian	P1015	2023-11-14	47	other	former	26.2	130	84	182.6	41.8	109.3	no	no	moderate	CLN005	Advised on exercise and diet.
A00017	very high	100	true	Black	P1016	2022-08-30	75	male	former	39.1	165	111	359.3	16.7	260.2	yes	yes	low	CLN004	Critical risk, urgent specialist referral.
A00018	low	7	false	White	P1017	2023-06-04	31	female	never	22.4	115	70	142.7	60.3	74.2	no	no	high	CLN001	No risk factors, healthy.
A00019	moderate	33.4	true	Hispanic	P1018	2023-07-12	49	unknown	unknown	31.3	136	83	204.9	35	128.5	no	unknown	unknown	SYS002	blank
A00020	low	12.7	false	White	P1019	2023-02-23	39	male	never	23.5	120	77	158.7	57.2	82.4	no	no	moderate	CLN007	Routine, healthy metrics.

What the 200 rows show

from the 200-row sample

Very high (risk category) stands out: mean risk_factor_score is 89.9, against 17.4 for the rest.

49%family_history_chronic_disease = true
24.4median risk_factor_score
3diabetes statuses
3hypertension statuses
4sexes
4smoking statuses

Mean risk_factor_score by risk_category200 rows

risk_factor_score200 rows, in bands of 10

Median 24.4, from 0.10 to 100.0.

ethnicity184 rows with a value · 16 left blank

White61
Asian36
Black33
Hispanic28
Other26

21 columns by typefrom the column list below

string 11
integer 3
float 5
date 1
boolean 1

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 11 columns
`assessment_id`	string	Unique identifier for each health risk assessment record.unique	A00001
`patient_id`	string	Unique identifier for the patient being assessed.	P1001
`sex`	string	Sex of the patient.male · female · other · unknown	female
`ethnicity`	string	Ethnic background of the patient.5 ethnicities · optional	White
`smoking_status`	string	Patient's smoking status at the time of assessment.never · former · current · unknown · optional	never
`diabetes_status`	string	Indicates if the patient has a diagnosis of diabetes.yes · no · unknown · optional	no
`hypertension_status`	string	Indicates if the patient has a diagnosis of hypertension.yes · no · unknown · optional	no
`physical_activity_level`	string	Patient's self-reported physical activity level.low · moderate · high · unknown · optional	high
`risk_category`	string	Categorized risk level based on the risk factor score.low · moderate · high · very high	low
`assessor_id`	string	Identifier for the clinician or system that performed the assessment.optional	CLN001
`notes`	string	Optional free-text notes or comments from the assessor.optional	Routine assessment.
Numbers 8 columns
`age`	integer	Age of the patient at the time of assessment.0 to 120	27
`bmi`	float	Body Mass Index of the patient at assessment.10 to 80 · optional	21.5
`systolic_bp`	integer	Systolic blood pressure (mmHg) at assessment.60 to 250 · optional	112
`diastolic_bp`	integer	Diastolic blood pressure (mmHg) at assessment.30 to 150 · optional	74
`cholesterol_total`	float	Total cholesterol level (mg/dL) at assessment.50 to 400 · optional	146.2
`hdl_cholesterol`	float	HDL cholesterol level (mg/dL) at assessment.10 to 150 · optional	62.8
`ldl_cholesterol`	float	LDL cholesterol level (mg/dL) at assessment.10 to 300 · optional	84.9
`risk_factor_score`	float	Calculated composite risk score for chronic disease (e.g., 0-1 or 0-100 scale).0 or more	7.4
Dates and times 1 column
`assessment_date`	date	Date when the health risk assessment was conducted.	2023-02-15
True or false 1 column
`family_history_chronic_disease`	boolean	Whether the patient has a family history of chronic disease.optional	false

Use it for

A healthcare dashboard
The family_history_chronic_disease rate, risk_factor_score by risk_category and a breakdown of ethnicity. Excel, Power BI or Tableau.
Why do the 60 very high rows have a mean risk_factor_score of 89.9?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Assessments200
A000017.4low
A0000247.2high
A0000492.6very high
A software demo
Believable assessments with patient_id, assessment_date and age to fill a screen in front of a buyer.

This dataset200 rows21 columns

Yours1,000 rows21 columns

blueprint · patient-health-risk-factor-scores

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 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

Include age, BMI, blood pressure, and smoking status
Risk score calculated per patient
Flag high-risk profiles
No PHI or direct identifiers

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-health-risk-factor-scores

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?