AI Algorithm Reads and Predicts Patient Data From Electronic Health Records

"Scientists at the Icahn School of Medicine at Mount Sinai have developed a new, automated, artificial intelligence (AI)-based algorithm that can read and predict patient data from electronic health records (EHRs).

The new method is called Phe2vec, and it can accurately identify patients with certain diseases. It was demonstrated to be just as accurate as the most popular traditional method, which requires more manual labor to perform.

Benjamin S. Glicksberg, PhD, is Assistant Professor of Genetics and Genomic Sciences. He is also a member of the Hasso Plattner Institute for Digital Health at Mount Sinai (HPIMS) and a senior author of the study.

“There continues to be an explosion in the amount and types of data electronically stored in a patient’s medical record. Disentangling this complex web of data can be highly burdensome, thus slowing advancements in clinical research,” said Glicksberg. “In this study, we created a new method for mining data from electronic health records with machine learning that is faster and less labor intensive than the industry standard. We hope that this will be a valuable tool that will facilitate further, and less biased, research in clinical informatics.”

The study, which was published in the journal Patterns, was led by Jessica K. De Freitas, a graduate student in Dr. Glicksberg’s lab..." Lire la suite