EndlessMedical API uses synthetic individual patient-level data for AI modeling
Endless Medical API uses patent-pending technologies (USPTO # 20200118691, PCT/US2019/055747) to generate a knowledge database containing synthetic, individual patient- level data based on research, clinical experience and literature.
Given recent concerns about transparency and data integrity (with 2 recent publications in Lancet and NEJM) with big-data, we encourage everybody to review the patents information to make sure they understand the advantages and disadvantages of EndlessMedical API technologies.
EndlessMedical API uses synthetic, individual patient-level data
EndlessMedical API does not use "big-data". Research and experience repeatedly show that big-data is inaccurate, has errors, typos, and biases, brings security, privacy and data ownership concerns.
We are generating and using synthetic patient-level data for AI/ML modeling to overcome these of the big-data issues.
Big-data under-represents rare diseases and tends to over-represent billable diagnoses and pertinent positive findings in physical examination and history, ignoring other normal and negative findings.
Big-data acquisition for research or commercial use requires costly and time-consuming IRB approvals.
Synthethic patient-level data is not perfect either, but maybe an alternative to big-data for some usages. For example, Endless Medical API uses these data for AI/ML modeling.
Endless Medical API technologies, allow to create synthethic databases echoing the true prevalence of diseases and clinical findings in a given environment.
For example, the incidence of strep throat in general practice office is different than the incidence of strep throat in nursing homes. Historically, AI and ML models trained on data from one environment can not be effectively used in another. EndlessMedical technologies would allow to transpose the data from one environment to another.
The EndlessMedical API's knowledge database, currently is created only by Lukasz Kiljanek MD and his review of literature when deemed necessary. It can, on-demand, be transposed, to a different clinical setting, like an intensive care unit or an emergency department.
In the future, data sourced from multiple experts or data sources (i.e., research papers) will be merged to increase the robustness of the medical knowledge database and thereof sensitivity and specificity of AI/ML models.
API receives patient's symptoms
API receives patient's signs
API receives patient's blood test results
API receives patient's chest film results
API receives patient's brain CT scan results
API receives patient's back MRI scan results
API returns the differential diagnoses list
API returns the list of diagnoses that can't be missed
API triages patient to the right specialist
API returns the list of recommended tests
API returns the risk of immediately life-threatening disease
API returns the next best diagnostic test
API returns the next best question to be asked to patient
API returns the dcumentation of the encounter
API returns billing suggestions
PRE-DIAGNOSE PATIENTS TRIAGE TO SPECIALTY CARE OPTIMIZE WORKUP TRIAGE BY ACUITY