Reduction of Readmissions to Hospitals Based on Actionable Knowledge Discovery and Personalization
In recent years, healthcare spending has risen and become a burden on governments especially in the US. One of the reasons for this increase is hospital readmissions, which is defined as a re-hospitalization of a patient after being discharged from a hospital within a short period of time. Decreasing the number of readmissions can improve the healthcare quality and reduce the healthcare spending. This research talk will cover the methods and the algorithms developed to reduce the number of readmissions by applying the concept of personalization and actionable patterns. Several machine learning algorithms have been used to predict the risk of mortality and readmission. Additionally, novel algorithms have been developed to extract all possible procedure paths during the course of treatment, cluster patients according to the similarities in their diagnoses, and evaluate the procedures and the clusters to anticipate the average number of readmissions for new patients and help the physicians in their decision making.
Mamoun Al-Mardini is a doctorate candidate in the Department of Computer Science at the University of North Carolina-Charlotte. His research interest lies in the broad area of Data Science with an emphasis on Health Analytics. His primary research concerns analyzing big data and extracting insightful knowledge. Mr. Al-Mardini finished Masters of Science (M.S.) in Computer Engineering from the American University of Sharjah, and Bachelor of Science (B.S.) in Computer Engineering from the Jordan University of Science and Technology. He held different positions in the industry and academia and most recently worked as a software engineer intern in the field of Internet of Things (IoT) at Cisco Systems.