Isaac Kohane

Bending the Learning Curve for EHR Research via Knowledge-Driven Online Multimodal Automated Phenotyping System

Xin Xiong, Sara Morini Sweet, Chuan Hong, Clara-Lea Bonzel et autres

Electronic health records (EHRs) hold great promise for translational research but remain difficult to use at scale because diagnostic codes are noisy, disease-relevant features are hard to identify, high-quality labels are limited and patient-level data sharing is often restricted. We introduce Knowledge-driven …

Bending the Learning Curve for EHR Research via Knowledge-Driven Online Multimodal Automated Phenotyping System

Xin Xiong, Sara Morini Sweet, Chuan Hong, Clara-Lea Bonzel et autres

Electronic health records (EHRs) hold great promise for translational research but remain difficult to use at scale because diagnostic codes are noisy, disease-relevant features are hard to identify, high-quality labels are limited and patient-level data sharing is often restricted. We introduce Knowledge-driven …

Bending the Learning Curve for EHR Research via Knowledge-Driven Online Multimodal Automated Phenotyping System

Xin Xiong, Sara Morini Sweet, Chuan Hong, Clara-Lea Bonzel et autres

Electronic health records (EHRs) hold great promise for translational research but remain difficult to use at scale because diagnostic codes are noisy, disease-relevant features are hard to identify, high-quality labels are limited and patient-level data sharing is often restricted. We introduce Knowledge-driven …

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A Multi-Agent Large Language Model Reasoning Engine for Early Detection of Pediatric Growth Disorders

Naveed Rabbani, Jannik Mettner, Kyungjoon Lee, Carmen L. Soto-Rivera et autres

Routine childhood growth surveillance is a cornerstone of pediatric care. Growth pattern abnormalities are often early manifestations of chronic disease. Yet subtle abnormalities are frequently underrecognized, leading to diagnostic delays and avoidable morbidity. We introduce SPROUT (System for Pediatric Recognition Of Undiagnosed …

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MEDS — An Emerging Data Standard and Ecosystem for Health AI Research

Matthew B. A. McDermott, Ethan Steinberg, Jason A. Fries, Robin van de Water et autres

While data standards have been well adopted and highly impactful for observational health informatics, the emerging application of artificial intelligence (AI) to electronic health record (EHR) data - known broadly as health AI - still lacks broadly adopted data standards. This gap …

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Better Science, Worse Science: AI Is Doing Both to Medical Publishing

Isaac Kohane, Douglas Flora

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From prediction to navigation for artificial intelligence in medicine

Girish N Nadkarni, Jolion McGreevy, Nicholas Gavin, Robert Freeman et autres

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EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining over Electronic Health Records

Payal Chandak, Gregory Kondas, Liat Antwarg Friedman, Isaac Kohane et autres

Foundation models pretrained on electronic health records (EHR) have demonstrated zero-shot clinical prediction capabilities by generating synthetic patient futures and aggregating statistics over sampled trajectories. However, this autoregressive inference procedure is computationally expensive, statistically noisy, and not natively promptable because users cannot …

EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining over Electronic Health Records

Payal Chandak, Gregory Kondas, Liat Antwarg Friedman, Isaac Kohane et autres

Foundation models pretrained on electronic health records (EHR) have demonstrated zero-shot clinical prediction capabilities by generating synthetic patient futures and aggregating statistics over sampled trajectories. However, this autoregressive inference procedure is computationally expensive, statistically noisy, and not natively promptable because users cannot …

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What do LLMs value? An evaluation framework for revealing subjective trade-offs in assessment of glycemic control.

Payal Chandak, Healey Ea, María F. Villa-Tamayo, Agatha F. Scheideman et autres

Clinical decisions often require balancing conflicting priorities rather than simply selecting a single "correct" answer. We present an evaluation framework that probes the value judgments embedded in large language models (LLMs) by testing how they assess quality of glycemic control from continuous …

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The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

Eric Venner, Karynne Patterson, Divya Kalra, Marsha M. Wheeler et autres

Disparities in data underlying clinical genomic interpretation is an acknowledged problem, but there is a paucity of data demonstrating it. The All of Us Research Program is collecting data including whole-genome sequences, health records, and surveys for at least a million participants …

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