Mount Sinai Develops AI System to Enhance Rare Disease Detection

Mount Sinai researchers plan to make InfEHR’s code available to other institutions to further explore its applications in personalized treatment and research.

New York City-based Icahn School of Medicine at Mount Sinai researchers have developed an artificial intelligence system designed to improve diagnostic accuracy by linking unconnected medical events over time.

The system, known as Inference on Electronic Health Records (InfEHR), connects scattered data in electronic health records (EHRs) to uncover hidden patterns that may indicate underlying diseases, according to an October 15 news release from the health system. It was designed by Mount Sinai’s Windreich Department of Artificial Intelligence and Human Health in collaboration with other institutions.

In a study published on September 26 in Nature Communications, InfEHR analyzed deidentified records from Mount Sinai and UC Irvine hospitals. The AI tool identified neonatal sepsis 12 to 16 times more accurately and postoperative kidney injury 4 to 7 times more accurately than current diagnostic methods.

Mount Sinai researchers plan to make InfEHR’s code available to other institutions to further explore its applications in personalized treatment and research.

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