New York, NY [October 15, 2025]—Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly—especially for patients with rare diseases or unusual symptoms.
Now, researchers at the Icahn School of Medicine at Mount Sinai and collaborators have developed an artificial intelligence system, called InfEHR, that links unconnected medical events over time, creating a diagnostic web that reveals hidden patterns. Published in the September 26 online issue of Nature Communications , the study shows that Inference on Electronic Health Records (InfEHR) transforms millions of scattered data points into actionable, patient-specific diagnostic insights.
"We were intrigued by how often the system rediscovered patterns that clinicians suspected but couldn't act on because the evidence wasn't fully established," says senior corresponding author Girish N. Nadkarni, MD, MPH , Chair of the Windreich Department of Artificial Intelligence and Human Health , Director of the Hasso Plattner Institute for Digital Health , the Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai, and the Chief AI Officer of the Mount Sinai Health System. "By quantifying those intuitions, InfEHR gives us a way to validate what was previously just a hunch and opens the door to entirely new discoveries."
Most medical artificial intelligence (AI), no matter how advanced, applies the same diagnostic process to every patient. InfEHR works differently by tailoring its analysis to each individual. The system builds a network from a patient's specific medical events and their connections over time, allowing it to not only provide personalized answers but also to ask personalized questions. By adapting both what it looks for and how it looks, InfEHR brings personalized diagnostics within reach, the investigators say.
In the study, InfEHR analyzed deidentified, privacy-protected electronic records from two hospital systems (Mount Sinai in New York and UC Irvine in California). The investigators turned each patient's medical timeline—visits, lab tests, medications, vital signs—into a network that showed how events connected over time. The AI studied many of these networks to learn which combinations of clues tend to appear when a hidden condition is present.
With a small set of doctor-confirmed examples to calibrate it, the system checked whether it could correctly flag two real-world problems: newborns who develop sepsis despite negative blood cultures and patients who develop a kidney injury after surgery. Its performance in identifying patients with the diagnosis was compared with current clinical rules and validated across both hospitals. Notably, the system could also signal when the record lacked sufficient information, allowing it to respond "not sure" as a safety feature.
The study found that InfEHR can detect disease patterns that are invisible when examining isolated data. For neonatal sepsis without positive blood cultures—a rare, life-threatening condition—InfEHR was 12–16 times more likely to identify affected infants than current methods. For postoperative kidney injury, the system flagged at-risk patients 4–7 times more effectively. Importantly, InfEHR achieved this without needing large amounts of training data, learning directly from patient records and adapting across hospitals and populations.
"Traditional AI asks, 'Does this patient resemble others with the disease?' InfEHR takes a different approach: 'Could this patient's unique medical trajectory result from an underlying disease process?' It's the difference between simply matching patterns and uncovering causation," says lead author Justin Kauffman, MS, Senior Data Scientist at the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine.
Importantly, in addition, InfEHR flags how confident it is in its predictions. Unlike other AI that may give a wrong answer with certainty, InfEHR knows when to say, 'I don't know'—a key safety feature for real-world clinical use, say the investigators.
The team is making the coding of InfEHR available to other researchers as it continues to study uses of the system. For example, the team will next explore how InfEHR could personalize treatment decisions by learning from clinical trial data and extending those insights to patients whose specific characteristics or symptoms were not fully represented in the original trials.
"Clinical trials often focus on specific populations, while doctors care for every patient," Mr. Kauffman says. "Our probabilistic approach helps bridge that gap, making it easier for clinicians to see which research findings truly apply to the patient in front of them."
The paper is titled "InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records." The study's authors, as listed in the journal, are Justin Kauffman, Emma Holmes, Akhil Vaid, Alexander W. Charney, Patricia Kovatch, Joshua Lampert, Ankit Sakhuja, Marinka Zitnik, Benjamin S. Glicksberg, Ira Hofer, and Girish N. Nadkarni.
This work was supported in part by the National Institutes of Health grant UL1TR004419, and the Clinical and Translational Science Awards grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under awards S10OD026880 and S10OD030463.
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