US 11,721,430 B1
Methods, systems, and computer readable media for using machine learning in detecting drug diversion
Rebecca Ann Schroeder, Durham, NC (US); Nigel Benjamin Neely, Durham, NC (US); Timothy William Dunn, Durham, NC (US); Evan S. Frasure, III, Cary, NC (US); Erich Senin Huang, Durham, NC (US); and Joseph Puthenveetil Mathew, Durham, NC (US)
Assigned to Duke University, Durham, NC (US)
Filed by Duke University, Durham, NC (US)
Filed on Oct. 16, 2020, as Appl. No. 17/72,975.
Claims priority of provisional application 62/916,536, filed on Oct. 17, 2019.
Int. Cl. G16H 40/20 (2018.01); G16H 20/13 (2018.01); G16H 50/20 (2018.01); G16H 70/20 (2018.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01); G06Q 10/105 (2023.01); G06Q 10/0639 (2023.01); G06N 20/20 (2019.01); G16H 70/40 (2018.01); A61J 7/00 (2006.01)
CPC G16H 40/20 (2018.01) [G06N 20/20 (2019.01); G06Q 10/06398 (2013.01); G06Q 10/105 (2013.01); G16H 10/60 (2018.01); G16H 20/13 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/20 (2018.01); G16H 70/40 (2018.01); A61J 7/0076 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for using machine learning in detecting drug diversion, the method comprising:
at a computing platform:
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 including a neural network trained using one or more data sets associated with historical drug dispensation events including a data set indicating dispensation events and corresponding drug dispensation amounts for the dispensation events; and
outputting, by the drug diversion detection algorithm, information indicating that the observed drug dispensation amount is aberrant.