Introduction
A breakdown of key endpoints and data structures used by the API
This section will describe the schema of key objects used for the request and returned in the response by the Health Scoring API as well as ways to present the scores to users. Sub sections provide information about other endpoints for generating scores and related output.
Default Health Scoring Endpoint
The default health scoring endpoint on the API allows you to generate point in time scores for more than one user in a batch. Full details of the endpoint and its definition, along with tools for testing it can be found under the API Reference page.
Request
When sending user data for scoring the key object used will be the Predictors object. This is where you able to specify the predictors and biomarkers for a user. This is specified in an array of predictorobjects using the observation_code parameter. The list of possible biomarkers and predictors that are supported by the API can be found on the next page. We support the use of LOINCs for specifying biomarkers and predictors to be used for scoring (where a mapping is available), as well as our own observation codes.
Mode of operation
The API supports 3 different modes when submitting a request. This is specified in the mode property in the scoring_event_metadata node of the input request object sent to the API. These are:
score- Simply take the input biomarker data and generate scores for the data.validate- This option will only validate the input biomarker data to ensure the values are in the correct range for the given unit for the biomarker, and the gender of the user. No scores will be generated when using this option.validate_and_score- This is a combination of the previous 2 options. This will validate each biomarker and generate scores based on the input data. The validations will be returned alongside each scoring event result for a user.
{
"scoring_event_metadata": {
"event_id": "JSUHD79SH_20260128113614", // Unique identifier for the scoring event. This could correspond to a lab test or panel.
"event_asof_dtutc": "2026-01-17T21:21:51.632Z", // Date of the event or date of the lab test.
"mode": "validate_and_score"
},
"user_data": [
{
"uid_ext": "JSUHD79SH", //An ID for the user as it may exist in your system
"predictors": [
{
"id": "6b0ca208-86a7-440d-b0c1-0818ad53002e", //A unique ID for this biomarker in this scoring event. For example this ID coulbe for the instance of the biomarker included in the lab test for this user.
"observation_code": "30525-0",
"value": "28",
"unit": "a"
},
{
"id": "aa653593-7071-4266-b010-953cefff9eb3",
"observation_code": "46098-0",
"value": "male",
"unit": ""
},
{
"id": "c40aa00e-2e3d-4e7e-9718-95a25c2df8e9",
"observation_code": "8302-2",
"value": "78",
"unit": "inches"
},
{
"id": "1f796753-f375-494a-8539-b0f140ff4f56",
"observation_code": "29463-7",
"value": "210",
"unit": "lbs"
},
{
"id": "8835a752-bc03-473c-a4ba-37387c045f14",
"observation_code": "64219-9",
"value": "0",
"unit": "{pack}/d.a"
},
{
"id": "0dd946b0-d10a-477a-8a43-dc313e0ef0a9",
"observation_code": "4548-4",
"value": "5.4",
"unit": "%"
},
{
"id": "a624e601-d519-40d9-b972-b45e15d2a43e",
"observation_code": "2885-2",
"value": "8.1",
"unit": "g/dL"
},
{
"id": "3197ae19-9438-40d5-aded-67228b498a9e",
"observation_code": "2571-8",
"value": "40",
"unit": "mg/dL"
},
{
"id": "3d9b1dcd-3c70-4c36-9f7b-fc8e2e57bd49",
"observation_code": "10835-7",
"value": "6.2",
"unit": "mg/dL"
},
{
"id": "29ebacd2-baf0-4b86-b66d-92ece13264e3",
"observation_code": "1751-7",
"value": "4.8",
"unit": "g/dL"
},
{
"id": "f644ac87-420c-4656-8f07-daa6350bca0c",
"observation_code": "17861-6",
"value": "9.6",
"unit": "mg/dL"
},
{
"id": "0dfe36d5-a8d3-4a36-9d6d-7dcec20e647d",
"observation_code": "2160-0",
"value": "0.83",
"unit": "mg/dL"
},
{
"id": "c6f4d776-d8e8-486a-9ed0-2d6260c0a583",
"observation_code": "2498-4",
"value": "106",
"unit": "ug/dL"
},
{
"id": "bd9d03b9-1be7-4e51-a3c9-5fd7dee92ea8",
"observation_code": "2086-7",
"value": "76",
"unit": "mg/dL"
},
{
"id": "852a0141-90a0-4397-9480-081786fedc54",
"observation_code": "1975-2",
"value": "0.7",
"unit": "mg/dL"
},
{
"id": "aed5cd82-513a-47d8-b8ae-7109dc8a8065",
"observation_code": "13457-7",
"value": "80",
"unit": "mg/dL"
}
]
}
]
}Response
The response from the scoring event is made up of key objects that provide information about a user's health across different health domains.
1. scoring_event_metadata
Contains information about the request being sent.
| Field | Description |
|---|---|
event_id | A unique identifier to track the event. Passed back in the output as-is. |
event_asof_dtutc | A datetime for when the event was sent. |
mode | Scoring behavior mode. |
2. user_data
An array of scoring events. Each entry contains the following properties:
uid_ext
The ID for the user being scored — an ID from your system used to track the user.
scoring_predictors
An array of predictor metadata objects used to score upon. Returned every time, including both given predictors (provided in the request) and sideways imputed predictors. Predictors are returned as LOINCs (matching the input format).
| Field | Description |
|---|---|
predictor_code | The code of the predictor. |
values | The value of the predictor. |
imputation_code | Where the predictor came from: GIVEN, FORWARD, BACKWARD, or SIDEWAYS. |
percentiles | The percentile for the predictor — what percentage of predictors for people in the same sex/age group this person's predictor is higher than. |
disease_scores
An array of objects containing scores associated with a particular health domain. See the default list of health domains.
| Field | Description |
|---|---|
disease_code | The code for the health domain. |
age_to_score | The age the disease was scored at. |
info_ratio | The information ratio for the disease. |
score_percentile | The percentage of people in the same sex/age group that this user has a higher Volo Score than. |
health_score
The Volo Score for the user for the particular health domain.
| Field | Description |
|---|---|
health_score | The Volo Score for the disease. |
min_score | The minimum score for the disease (currently varies by gender only). |
max_score | The maximum score for the disease (currently varies by gender only). |
risk_ratios
The risk ratios for the user, associated with the health domain as calculated by the model.
| Field | Description |
|---|---|
your_risk_frac | The user's risk for the health domain. |
peer_risk_frac | The average risk of people within the user's sex/age group for the health domain. |
risk_ratio | The risk ratio for the health domain. |
disease_age
The Volo Age for the disease.
| Field | Description |
|---|---|
disease_age | The biological age for the disease. |
disease_age_delta | The difference between disease_age and the age the model scored at. |
predictor_attributions
An array of objects containing details about the attribution of the score to a particular predictor.
| Field | Description |
|---|---|
predictor_code | The code of the predictor. |
multiplier | The multiplier on the health score. Values > 1 indicate the predictor is decreasing risk; values < 1 indicate the predictor is increasing risk. The product of all attributions equals the inverse of risk_ratio. |
score_attribution | The portion of the score attributed to this predictor. All values sum to health_score. |
risk_attribution | The portion of the risk ratio attributed to this predictor. All values sum to risk_ratio. |
errors
A list of any errors or issues encountered during scoring for a particular user. Returned as an array of strings.
3. Validations
ValidationsAn array of objects returning validation information for each biomarker/predictor sent in the request.
| Field | Description |
|---|---|
id | The ID of the predictor that was validated. |
obs_code | The observation code validated (internal codes mapping to input predictor/biomarker codes). See the full list. |
obs_code_ext | Extended observation code (e.g. the associated LOINC), if available. |
qa_status | Status of the validation process: PASS, FLAGGED, or FAIL. |
message | Additional information about the validation result. |
validated_value | The converted, acceptable value for the predictor based on the preferred UCUM unit. |
validated_unit | The preferred UCUM unit the converted value conforms to. |
return_code Values
| Code | Description |
|---|---|
valid_entry | Entry passes all validation checks. |
invalid_code | LOINC or observation code not found in database. |
invalid_value | Value format or content is invalid (used by custom validators). |
invalid_ucum_unit | Unit doesn't match expected unit for biomarker. |
not_pref_unit | Value provided in a non-preferred but convertible unit; conversion succeeded. |
invalid_unit_dimension | Unit doesn't match expected unit and conversion failed (incompatible or invalid unit). |
value_over_implausible_maximum | Value is below the implausible lower threshold. |
value_under_implausible_minimum | Value is above the implausible upper threshold. |
value_over_borderline_maximum | Value is below the borderline lower threshold. |
value_under_borderline_minimum | Value is above the borderline upper threshold. |
value_over_unusual_maximum | Value is below the unusual lower threshold. |
value_under_unusual_minimum | Value is above the unusual upper threshold. |
severity Levels
| Level | Description |
|---|---|
IMPLAUSIBLE | Entry is definitely wrong (most severe). |
BORDERLINE | Entry is questionable with low confidence (moderate severity). |
UNUSUAL | Entry is uncommon but potentially valid (least severe). |
None | Entry passed; no severity level needed. |
{
"scoring_event_metadata": {
"event_id": "SPOT1WJOQH_1768684911632",
"event_asof_dtutc": "2026-01-17T21:21:51.632Z",
"product_version": "4.3.0"
},
"scoring_results": [
{
"uid_ext": "SPOT1WJOQH",
"data": [
{
"scoring_predictors": [
{
"predictor_code": "srSmoke__Pack_years_of_smoking",
"value": 0.0,
"imputation_code": "GIVEN",
"percentile": 59.60410557184751
},
{
"predictor_code": "41909-3",
"value": 24.265285996055226,
"imputation_code": "GIVEN",
"percentile": 21.901236452450007
},
{
"predictor_code": "8302-2",
"value": 78.0,
"imputation_code": "GIVEN",
"percentile": 99.58784918333079
},
{
"predictor_code": "8462-4",
"value": 71.06679400032827,
"imputation_code": "SIDEWAYS",
"percentile": 40.24121913454486
},
{
"predictor_code": "8480-6",
"value": 119.67778583214007,
"imputation_code": "SIDEWAYS",
"percentile": 49.92054113524306
},
{
"predictor_code": "pm__grip_strength_mean_lbs",
"value": 104.92213163888103,
"imputation_code": "SIDEWAYS",
"percentile": 68.78127028988274
},
{
"predictor_code": "40443-4",
"value": 64.85154564239221,
"imputation_code": "SIDEWAYS",
"percentile": 45.11449759595795
},
{
"predictor_code": "1751-7",
"value": 4.8,
"imputation_code": "GIVEN",
"percentile": 70.74458722055977
},
{
"predictor_code": "6768-6",
"value": 78.32361224268618,
"imputation_code": "SIDEWAYS",
"percentile": 50.9687880016214
},
{
"predictor_code": "1742-6",
"value": 23.055872833565854,
"imputation_code": "SIDEWAYS",
"percentile": 39.33760857212436
},
{
"predictor_code": "1884-6",
"value": 53.13061480463352,
"imputation_code": "SIDEWAYS",
"percentile": 0.765703054622881
},
{
"predictor_code": "1920-8",
"value": 25.677798707107822,
"imputation_code": "SIDEWAYS",
"percentile": 45.38492871690428
},
{
"predictor_code": "3094-0",
"value": 14.45137291812994,
"imputation_code": "SIDEWAYS",
"percentile": 52.15256644349767
},
{
"predictor_code": "17861-6",
"value": 9.6,
"imputation_code": "GIVEN",
"percentile": 53.590770992030286
},
{
"predictor_code": "2160-0",
"value": 0.83,
"imputation_code": "GIVEN",
"percentile": 30.266039419255414
},
{
"predictor_code": "1988-5",
"value": 1.069482469810572,
"imputation_code": "SIDEWAYS",
"percentile": 50.780551264330434
},
{
"predictor_code": "33863-2",
"value": 0.8914020156971584,
"imputation_code": "SIDEWAYS",
"percentile": 62.52077674626019
},
{
"predictor_code": "2324-2",
"value": 26.081394886274428,
"imputation_code": "SIDEWAYS",
"percentile": 36.285807027509534
},
{
"predictor_code": "4548-4",
"value": 5.4,
"imputation_code": "GIVEN",
"percentile": 69.72225630956763
},
{
"predictor_code": "2086-7",
"value": 76.0,
"imputation_code": "GIVEN",
"percentile": 98.06573845611561
},
{
"predictor_code": "2484-4",
"value": 189.45185241694236,
"imputation_code": "SIDEWAYS",
"percentile": 56.837641686373644
},
{
"predictor_code": "13457-7",
"value": 80.0,
"imputation_code": "GIVEN",
"percentile": 7.2195121951219505
},
{
"predictor_code": "43583-4",
"value": 38.899797186371984,
"imputation_code": "SIDEWAYS",
"percentile": 64.92955612785941
},
{
"predictor_code": "13967-5",
"value": 40.73939455564831,
"imputation_code": "SIDEWAYS",
"percentile": 77.00594024292934
},
{
"predictor_code": "1975-2",
"value": 0.7,
"imputation_code": "GIVEN",
"percentile": 75.76276019389792
},
{
"predictor_code": "2986-8",
"value": 564.9862336650463,
"imputation_code": "SIDEWAYS",
"percentile": 73.96974652493867
},
{
"predictor_code": "2885-2",
"value": 8.1,
"imputation_code": "GIVEN",
"percentile": 95.93725213712875
},
{
"predictor_code": "2571-8",
"value": 40.0,
"imputation_code": "GIVEN",
"percentile": 0.40196516301920504
},
{
"predictor_code": "3084-1",
"value": 5.196631280059059,
"imputation_code": "SIDEWAYS",
"percentile": 28.246845457865057
},
{
"predictor_code": "14635-7",
"value": 41.61055142150268,
"imputation_code": "SIDEWAYS",
"percentile": 71.25589622641509
},
{
"predictor_code": "6690-2",
"value": 5.822673672262803,
"imputation_code": "SIDEWAYS",
"percentile": 35.287553305966284
},
{
"predictor_code": "789-8",
"value": 4.853367372418893,
"imputation_code": "SIDEWAYS",
"percentile": 51.41684269259894
},
{
"predictor_code": "30350-3",
"value": 15.122401112344287,
"imputation_code": "SIDEWAYS",
"percentile": 50.10561555936391
},
{
"predictor_code": "20570-8",
"value": 43.60129333093285,
"imputation_code": "SIDEWAYS",
"percentile": 50.04184767446495
},
{
"predictor_code": "30428-7",
"value": 89.59648551762287,
"imputation_code": "SIDEWAYS",
"percentile": 43.995855087481566
},
{
"predictor_code": "785-6",
"value": 31.067829004505402,
"imputation_code": "SIDEWAYS",
"percentile": 42.9157865370053
},
{
"predictor_code": "28540-3",
"value": 34.697917583819034,
"imputation_code": "SIDEWAYS",
"percentile": 53.429516559722614
},
{
"predictor_code": "788-0",
"value": 13.124054403810012,
"imputation_code": "SIDEWAYS",
"percentile": 48.75054800526085
},
{
"predictor_code": "29463-7",
"value": 210.0,
"imputation_code": "GIVEN",
"percentile": 76.60280873149138
},
{
"predictor_code": "bl__AST_ALT_Ratio",
"value": 1.1137205211214056,
"imputation_code": "SIDEWAYS",
"percentile": 61.39993481095176
},
{
"predictor_code": "bl__BUN_creat_ratio",
"value": 17.41129267244571,
"imputation_code": "SIDEWAYS",
"percentile": 65.64275279987015
},
{
"predictor_code": "bl__LDL_ApoB_ratio",
"value": 1.7783922170717612,
"imputation_code": "SIDEWAYS",
"percentile": 99.79477918240026
},
{
"predictor_code": "1869-7",
"value": 176.32270832334947,
"imputation_code": "SIDEWAYS",
"percentile": 95.0954038690345
},
{
"predictor_code": "2339-0",
"value": 85.7442385345538,
"imputation_code": "SIDEWAYS",
"percentile": 44.485569508702355
},
{
"predictor_code": "704-7",
"value": 24.966308749285176,
"imputation_code": "SIDEWAYS",
"percentile": 57.97396582015652
},
{
"predictor_code": "30180-4",
"value": 0.5109687423993962,
"imputation_code": "SIDEWAYS",
"percentile": 66.39514454559975
},
{
"predictor_code": "26449-9",
"value": 174.26475019921654,
"imputation_code": "SIDEWAYS",
"percentile": 54.74365117393387
},
{
"predictor_code": "26450-7",
"value": 2.9555012164866117,
"imputation_code": "SIDEWAYS",
"percentile": 63.228717457275195
},
{
"predictor_code": "731-0",
"value": 1706.8993098761314,
"imputation_code": "SIDEWAYS",
"percentile": 41.03977000479156
},
{
"predictor_code": "736-9",
"value": 29.406688180909338,
"imputation_code": "SIDEWAYS",
"percentile": 51.81280945535857
},
{
"predictor_code": "742-7",
"value": 440.8207987221936,
"imputation_code": "SIDEWAYS",
"percentile": 47.45647660118192
},
{
"predictor_code": "5905-5",
"value": 7.410427622835616,
"imputation_code": "SIDEWAYS",
"percentile": 55.99744449768408
},
{
"predictor_code": "751-8",
"value": 3546.980115963049,
"imputation_code": "SIDEWAYS",
"percentile": 41.19549592716819
},
{
"predictor_code": "23761-0",
"value": 59.68521370560717,
"imputation_code": "SIDEWAYS",
"percentile": 48.714262897300756
},
{
"predictor_code": "777-3",
"value": 225.54343377944326,
"imputation_code": "SIDEWAYS",
"percentile": 38.83065641066518
}
],
"disease_scores": [
{
"disease_code": "Liver",
"age_to_score": 0.0,
"info_ratio": 0.0,
"health_score": {
"health_score": 229.02287715399174,
"max_score": 250.0
},
"risk_ratios": {
"your_risk_frac": 0.003741230801645124,
"peer_risk_frac": 0.01022945109568367,
"risk_ratio": 0.36573133461909224
},
"disease_age": {
"disease_age": 21.0,
"disease_age_delta": -7.0
},
"predictor_attributions": [
{
"predictor_code": "30525-0",
"score_attribution": -36.0632908432867,
"risk_attribution": 0.788130523071485
},
{
"predictor_code": "srSmoke__Pack_years_of_smoking",
"score_attribution": 5.254817680861311,
"risk_attribution": -0.1148392759124328
},
{
"predictor_code": "63632-4",
"score_attribution": -0.0,
"risk_attribution": 0.0
},
{
"predictor_code": "8302-2",
"score_attribution": 4.737047221014627,
"risk_attribution": -0.10352387197097873
},
{
"predictor_code": "pm__grip_strength_mean_lbs",
"score_attribution": 12.921887367771593,
"risk_attribution": -0.28239613224671783
},
{
"predictor_code": "40443-4",
"score_attribution": 4.09475183536255,
"risk_attribution": -0.08948708868184067
},
{
"predictor_code": "1751-7",
"score_attribution": 3.505055283914883,
"risk_attribution": -0.0765998052232814
},
{
"predictor_code": "6768-6",
"score_attribution": 2.4426874980080395,
"risk_attribution": -0.05338272050299113
},
{
"predictor_code": "1742-6",
"score_attribution": 1.58161655325476,
"risk_attribution": -0.03456479573181372
},
{
"predictor_code": "1920-8",
"score_attribution": -0.50355269953141,
"risk_attribution": 0.011004687680897714
},
{
"predictor_code": "3094-0",
"score_attribution": 1.0274075232803448,
"risk_attribution": -0.02245305987878949
},
{
"predictor_code": "17861-6",
"score_attribution": -0.043967792451696505,
"risk_attribution": 0.0009608762387724441
},
{
"predictor_code": "2160-0",
"score_attribution": 0.9033605366478139,
"risk_attribution": -0.01974212545838461
},
{
"predictor_code": "1988-5",
"score_attribution": 2.676696993290948,
"risk_attribution": -0.05849678584778883
},
{
"predictor_code": "33863-2",
"score_attribution": 1.7149241759346032,
"risk_attribution": -0.03747811295648511
},
{
"predictor_code": "2324-2",
"score_attribution": -3.8378541882945343,
"risk_attribution": 0.08387282353229071
},
{
"predictor_code": "4548-4",
"score_attribution": 3.369813620484097,
"risk_attribution": -0.07364422129157805
},
{
"predictor_code": "2086-7",
"score_attribution": 6.429169146611463,
"risk_attribution": -0.14050366242095702
},
{
"predictor_code": "2484-4",
"score_attribution": 10.88117870941999,
"risk_attribution": -0.23779829481329473
},
{
"predictor_code": "43583-4",
"score_attribution": -1.281681093324002,
"risk_attribution": 0.028009978204202566
},
{
"predictor_code": "13967-5",
"score_attribution": 2.504414143586486,
"risk_attribution": -0.054731700375033096
},
{
"predictor_code": "1975-2",
"score_attribution": -2.7756185417764496,
"risk_attribution": 0.06065862659853199
},
{
"predictor_code": "2986-8",
"score_attribution": 1.7977927529623545,
"risk_attribution": -0.03928913057112493
},
{
"predictor_code": "2885-2",
"score_attribution": -13.148845468213452,
"risk_attribution": 0.2873560957507099
},
{
"predictor_code": "2571-8",
"score_attribution": 1.2420059161700332,
"risk_attribution": -0.027142913180680673
},
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],
"validations": [
{
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"severity": "IMPLAUSIBLE",
"message": "No threshold data for predictor code 'age' (resolved from '30525-0')",
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{
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"id": "1f796753-f375-494a-8539-b0f140ff4f56"
},
{
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"severity": "IMPLAUSIBLE",
"message": "No threshold data for predictor code 'srSmoke__Cigarette_packs_per_day' (resolved from '64219-9')",
"return_code": "no_threshold_data",
"id": "8835a752-bc03-473c-a4ba-37387c045f14"
},
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"return_code": "value_under_unusual_minimum",
"id": "3197ae19-9438-40d5-aded-67228b498a9e"
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{
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"message": "No threshold data for predictor code 'bl__Ferritin' (resolved from '2276-4')",
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]
}Each data object represents the scoring events for a particular user, represented by the uid_ext field. Any errors encountered during the scoring process will be written to the errors array (string). Any validations for the input data will be stored in the validations array against each user's scoring events.
Updated 15 days ago