Predictive Modeling of the Total Joint Replacement Surgery Risk: a Deep Learning Based Approach with Claims Data.

Total joint replacement (TJR) is one of the most commonly performed, fast-growing elective surgical procedures in the United States. Given its huge volume and cost variation, it has been regarded as one of the top opportunities to reduce health care cost by the industry. Identifying patients with a high chance of undergoing TJR surgery and engaging them for shopping is the key to success for plan sponsors.

Estimating disease burden using Internet data.

Data on disease burden are often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage and social media data, specifically the search volume on Google, page view count on Wikipedia, and disease mentioning frequency on Twitter, correlated with the disease burden, measured by prevalence and treatment cost, for 1633 diseases over an 11-year period. We also applied least absolute shrinkage and selection operator to predict the burden of diseases.

Development of a Behavioral Health Stigma Measure and Application of Machine Learning for Classification.

Given the growing public health importance of measuring the change in mental health stigma over time, the goal of this study was to demonstrate the potential for using machine learning as a tool to analyze patterns of social stigma as a complement to traditional research methods. A total of 1,904 participants were recruited through Sona Systems, Ltd (Tallinn, Estonia), an experiment management system for online research, to complete a self-reported survey. The collected data were used to develop a new measure of mental (behavioral) health stigma.

Systems modeling and simulation applications for critical care medicine.

Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective.