Accurately predict patient disease risk using Hurdles specially trained AI models for the top 10 mortality diseases (as of 2022). This includes:

Ischemic heart disease
Lung cancer, Stroke
Chronic obstructive pulmonary disease (COPD)
Alzheimer’s disease
Lower respiratory infection
Colorectal cancer, Breast cancer
Other Cardiovascular
Pancreatic cancer

Pass in a patient electronic health record (FHIR in JSON format) with as much information as possible about the patient. At minimum this should include Patient, Observation and any Condition.

It will return the patients probability of developing each of these 10 diseases over the next 10 years (at 90% confidence level).

Example FHIR input

{
   "resourceType":"Bundle",
   "id":"HurdlePatientExample",
   "entry":[
      {
         "resource":{
            "resourceType":"Patient",
            "id":"001",
            "gender":"male",
            "birthDate":"1944-11-17",
            "extension":[
               {
                  "extension":[
                     {
                        "url":"ombCategory",
                        "valueCoding":{
                           "code":"2135-2",
                           "system":"urn:oid:2.16.840.1.113883.6.238",
                           "display":"Hispanic or Latino"
                        }
                     }
                  ],
                  "url":"http://hl7.org/fhir/us/core/StructureDefinition/us-core-ethnicity"
               }
            ]
         }
      },
      {
         "resource":{
            "resourceType":"Observation",
            "code":{
               "coding":[
                  {
                     "system":"http://loinc.org",
                     "code":"39156-5",
                     "display":"Body mass index (BMI) [Ratio]"
                  }
               ]
            },
            "subject":{
               "reference":"Patient/001"
            },
            "effectiveDateTime":"2024-01-01",
            "valueQuantity":{
               "value":16.2,
               "system":"http://unitsofmeasure.org",
               "code":"kg/m2"
            }
         }
      },
      {
         "resource":{
            "resourceType":"Observation",
            "code":{
               "coding":[
                  {
                     "system":"http://loinc.org",
                     "code":"8302-2",
                     "display":"Body height"
                  }
               ]
            },
            "subject":{
               "reference":"Patient/001"
            },
            "effectiveDateTime":"2023-07-02",
            "valueQuantity":{
               "value":180,
               "system":"http://unitsofmeasure.org",
               "code":"cm"
            }
         }
      },
      {
         "resource":{
            "resourceType":"Observation",
            "code":{
               "coding":[
                  {
                     "system":"http://loinc.org",
                     "code":"29463-7",
                     "display":"Body Weight"
                  }
               ]
            },
            "subject":{
               "reference":"Patient/001"
            },
            "effectiveDateTime":"2023-06-15",
            "valueQuantity":{
               "value":70,
               "system":"http://unitsofmeasure.org",
               "code":"kg"
            }
         }
      },
      {
         "resource":{
            "resourceType":"Condition",
            "code":{
               "coding":[
                  {
                     "system":"http://snomed.info/sct",
                     "code":"414545008",
                     "display":"Ischemic heart disease"
                  }
               ]
            },
            "subject":{
               "reference":"Patient/001"
            },
            "onsetDateTime":"2024-03-28"
         }
      }
   ]
}

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