Applying machine learning approaches for predicting obesity risk using US health administrative claims database.

Introduction: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive predictive value (PPV) of BMI-related diagnosis codes using an electronic medical records (EMR) claims-linked database. Additionally, we applied machine learning (ML) to identify features in US claims databases to predict obesity status.

Identifying Predictors of PASI100 Responses up to Month 12 in Patients with Moderate-to-severe Psoriasis Receiving Biologics in the Psoriasis Study of Health Outcomes (PSoHO).

Despite the abundance of data concerning biologic treatments for patients with psoriasis, clinicians are often challenged with discerning the optimal treatment for each patient. To inform this selection, this study explored whether a patient's baseline characteristics or disease profile could predict the likelihood of achieving complete skin clearance with biologic treatment. Machine-learning and other statistical methods were applied to the substantial data collected from patients with moderate-to-severe psoriasis in the ongoing, international, prospective, observational Psoriasis Study of Health Outcomes (PSoHO).

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework.

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018.