Duke University

Joseph P Mathew

Mihai V Podgoreanu

Mark F Newman

Mark Stafford-Smith

William D White

Debra A Schwinn

Richard W Morris

Ian Welsby

Hilary P Grocott

Nigel Benjamin Neely

Rebecca Ann Schroeder

Mihal V Podgoreanu

Robert L Lobato

Erich Senin Huang

Miklos D Kertai

Yi-Ju Li

Evan S Frasure, Iii

Timothy William Dunn

According to one method for using machine learning in detecting drug diversion, the method comprises receiving, as input, an observed drug dispensation amount associated with a drug dispensation event related to a drug provider along with other drug dispensation event data, wherein the drug dispensation event data comprises drug provider information, procedure information, and patient information; generating, using a trained drug diversion detection algorithm and the drug dispensation event data, an expected drug dispensation amount associated with the drug dispensation event and determining, using the observed drug dispensation amount and the expected drug dispensation amount, whether the observed drug dispensation amount is aberrant, wherein the drug diversion detection algorithm includes at least one machine learning algorithm and is trained using one or more data sets associated with related drug dispensation events; and outputting, by the drug diversion detection algorithm, information indicating that the observed drug dispensation amount is aberrant.

Methods, systems, and computer readable media for using machine learning in detecting drug diversion

The present invention provides a method of identifying a human subject as having an increased risk of altered effectiveness of beta blocker therapy, comprising detecting in the subject one or more single nucleotide polymorphism associated with increased risk of altered effectiveness of beta blocker therapy.

Compositions and methods for identifying altered effectiveness of beta blocker therapy

The present invention relates, in general, to perioperative bleeding and, in particular, to methods of identifying individuals at risk of perioperative bleeding. The present invention relates, in general, to perioperative renal dysfunction and, in particular, to methods of identifying individuals at risk of perioperative renal dysfunction. The present invention relates, in general, to perioperative stroke and, in particular, to methods of identifying individuals at risk of perioperative stroke.

Methods of identifying individuals at risk of perioperative bleeding, renal dysfunction or stroke

The present invention relates, in general, to perioperative depression and, in particular, to methods of identifying individuals at risk of perioperative depression.

Predictors of long-term mortality following coronary artery bypass graft surgery

Growing community of inventors

Durham, NC, United States of America

Joseph P Mathew