Data Science in Healthcare

Between 2015 and 2017, I worked as a data scientist and full stack engineer at Dascena, a healthcare startup company based in the San Francisco Bay area. I led a small team of scientists and engineers to build clinical decision support tools with advanced machine learning techniques.

Clinical Decision Support Systems with A Machine Learning Approach

A clinical decision support system (CDSS) is a information technology system that assists heath professionals to perform clinical decision-making. CDSS is a major topic in artificial intelligence (AI) in healthcare.

Receiver operating characteristic curves for Dascena InSight versus competing methods at time of sepsis onset (Desautels et al 2016).

Many traditional clinical decision supports are based on simple scoring systems that only use tabulation or linear combinations of limited number of patient vital signs and laboratory results. These traditional systems are often inefficient and have limited predictive value. Modern computer-based predictive analytics have been demonstrated to improve diagnostic outcomes and reduce failures in care delivery, care coordination, and overtreatment.

At Dascena, I led a small team of scientists and engineers to utilize modern machine learning methods to develop the next generation clinical decision supports for various clinical applications, such as early prediction of sepsis onset, patient stability and mortality prediction and automated transfer recommendations, etc. Our team used freely accessible dataset such as MIMIC database, and we also collaborated with large hospitals to analyze their comprehensive datasets to develop data-driven, efficient, and secure clinical decision support tools which will benefit both health systems and patients.