Young

Multifactorial machine learning to recognize symptom patterns

Icon of a chain link, click to copy url to clipboard
Geographic

Summary

By leveraging the power of machine learning, this pilot is testing algorithms that can identify symptom patterns that a physician may not immediately associate with a rare disease. The concept is that technology may lead to a faster diagnosis by linking together subtle symptoms that may seem unrelated when viewed by physicians who have never seen a patient with the disease.


The technology will help analyze medical records data, patient-reported data, and genomics data. Upon successful completion of the pilot, the goal is to use this proof of concept to encourage integration into hospital EMR systems worldwide so that symptoms patterns are flagged, prompting a physician to consider testing for a rare disease.

To learn more or get involved, please contact Julian Isa Gomez

Little

Enable Collaboration Tools for “Intelligent Triage” and Clinical Geneticist Virtual Panel Consultation

Icon of a chain link, click to copy url to clipboard
Geographic

Summary

This pilot will utilize virtual collaboration tools and other health templates to design a reliable, multi-purpose platform that will allow genetic clinics to deliver genetic assessments and counseling remotely to patients and primary care physicians. Technology solutions such as facial recognition, virtual consultations, and a triaging system will be packaged on one uniform platform that will be piloted at Children’s National Hospital in Washington DC. If the pilot is successful, the platform will be available for adoption among clinics around the globe and will help to reduce time to diagnosis by minimizing the burden placed on patients in regard to time and cost spent on in-person consultations.

To learn more or get involved, please contact Carlos Pelayo

Small

Explore a Blockchain-based Patient Registry and Rare Disease Passport

Icon of a chain link, click to copy url to clipboard
Geographic

Summary

Secure technology will be used to create a platform that will empower patients as advocates by giving them control of their own patient data which could ultimately become part of a global patient registry for rare disease patients. Blockchain technology will be used to assure patient privacy and to manage patient consent. Patients will have their information stored in a manner that can be easily accessed, and the data will be owned by the patient, giving them the power they need to seek additional opinions and lead the pursuit of a diagnosis.

The first phase of this pilot is exploring a proof of concept using dummy patient data, with plans to expand into a trial phase with a partnering organization. Successful results from this pilot can ideally be leveraged by hospital systems, governments, and databases around the world.

To learn more or get involved, please contact Rune Wetlesen