By Stacey E. Grant, PharmD, VP of Clinical Operations In February, I had the opportunity to spend time with state lawmakers and advocate on behalf of the healthcare community for patients with chronic pain and patients in need of opioid use disorder (OUD) treatment during the Tennessee Pharmacists Association (TPA) Pharmacy Day on the Hill…

By David Simon, Ph.D., Research Data Scientist Predicting a patient’s risk for adverse outcomes is an important part of delivering personalized care and improving the lives of patients. In order to achieve these goals, axialHealthcare has developed a number of machine learning models that quantify patient risk. An exciting example is our machine learning model…

“We want to make sure that as we deal with the opioid crisis, we keep in mind those Americans who are hurting.” This statement was made by Senate Health Committee Chairman Senator Lamar Alexander during this week’s Health, Education, Labor & Pensions (HELP) Committee Hearing amidst increasing public discussion of the impact of the opioid…

By Meridith Peratikos, Director of Enterprise Research In January, researchers and technologists across a wide range of disciplines and job sectors converged at the RStudio::conf 2019 in Austin, Texas to celebrate and learn about R, a programming language for statistical computing and graphics, as well as discuss RStudio tools that make using R for business…

By Lindsey Morris, Ph.D., Director of Data Science and Analytics Sin City beckons tens of thousands of technology leaders from around the world to Amazon Web Service’s (AWS) re:Invent, a quintessential, annual technology conference where AWS reveals it’s cutting-edge technology offerings for the coming year. The advancements are always presented during keynote addresses in a minimalistic yet…

By May Shen, Ph.D., Research Data Scientist Machine learning models are being quickly adopted in various industry applications because of their outstanding performance compared to more traditional approaches like linear regression. However, machine learning models are also known as ‘black boxes’ because they provide little explanations for their predictions. The black-box nature of many machine learning…