Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.

Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients).

Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data.

Patients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. The ability to identify patients at high risk of DRE at the time of treatment initiation, and to subsequently steer their treatment pathway toward more personalized interventions, has high clinical utility. Here, we aim to demonstrate the feasibility of developing algorithms for predicting DRE using machine learning methods.

Changing the approach to treatment choice in epilepsy using big data.

Purpose: A UCB-IBM collaboration explored the application of machine learning to large claims databases to construct an algorithm for antiepileptic drug (AED) choice for individual patients.

Methods: Claims data were collected between January 2006 and September 2011 for patients with epilepsy > 16 years of age. A subset of patient claims with a valid index date of AED treatment change (new, add, or switch) were used to train the AED prediction model by retrospectively evaluating an index date treatment for subsequent treatment change.

Newer antiepileptic drug use and other factors decreasing hospital encounters.

A retrospective analysis was conducted in one claims database and was confirmed in a second independent database (covering both commercial and government insurance plans between 11/2009 and 9/2011) for the understanding of factors influencing antiepileptic drug (AED) use and the role of AEDs and other health-care factors in hospital encounters. In both datasets, epilepsy cases were identified by AED use and epilepsy diagnosis coding. Variables analyzed for effect on hospitalization rates were as follows: (1) use of first-generation AEDs or second-generation AEDs, (2) treatment changes, and (3) factors that may affect AED choice.