Maxeler Technologies recently licensed novel, “explainable” AI-driven prescription prediction technologies from Georgetown University. Using the patent-pending, technology developed at Georgetown’s Information Retrieval Laboratory under Professor Ophir Frieder, Maxeler intends to work with the researchers at Georgetown to productize scalable, patient-specific, prescription selectors, reducing drug resistance, thereby improving patient care. Frieder, the Robert L. McDevitt, K.S.G., K.C.H.S. and Catherine H. McDevitt L.C.H.S. Professor in Computer Science and Information Processing, is the lead inventor of the technology.
High dimensional information and temporal event relationships complicate the development of predictive models. Traditional approaches transform and “flatten” electronic health records into vector representations that ignore medical event temporal relationships, reducing prediction accuracy. The explainable approach automatically restructures each patient’s electronic medical record into a graph and utilizes a graph-kernel approach to formulate prescription predictions. As graphs are easily understood both by doctors and patients, the developed system will provide comprehensible explanations of why a particular medication is prescribed. The products are expected to address domestic and international markets.
Maxeler intends to propose using medical records from a national health service to start adapting the research models to real-world data. “Our goal is to develop a model to process electronic medical records relying on proven, scalable, data-mining techniques, yielding a nearly real-time, personalized, clinically explainable drug prediction approach,” says Oskar Mencer, CEO of Maxeler Technologies. Antibiotic prescriptions will be the initial focus. This is because real-time personalized prescriptions are needed as resistance to antibiotics is personalized and develops over time. As Frieder explains, “This partnership with Maxeler provides us with the opportunity to move our research from the abstract to clinical practice, hopefully globally improving patient care.”
