PPT-A Machine Learning Framework for Predicting Frequent Emergency Department Users

Using Claims Data Summer Xia Hu Margret Bjarnadottir Sean Barnes Bruce Golden University of Maryland College Park 1 POMS Conference May 06 2016 O rlando Florida Background Frequent Emergency . Gordon Machine Learning Department Carnegie Mellon University Pittsburgh Pennsylvania 15213 Abstract Recently a number of researchers have proposed spectral algorithms for learning models of dynam ical systemsfor example Hidden Markov Models HMMs Pa Machine: Adversarial Detection . of Malicious . Crowdsourcing Workers . Gang . Wang. , Tianyi Wang, Haitao . Zheng, Ben . Y. Zhao . UC Santa Barbara. gangw@cs.ucsb.edu. Machine Learning for Security. learning and prediction. Jongmin. Kim. Seoul National University. Problem statement. Predicting outcome of surgery. Predicting outcome of surgery. Ideal approach. . . . .. ?. Training Data. Predicting outcome. Thoracotomy. : A Hybrid Simulation With A Clinical Outcome. Actual ED . Thoracotomy. Footage. Relevance. :. At . Riverside Methodist Hospital, emergency . thoracotomies. are not an everyday occurrence. Emergency . Jim Welch, RMN.. Mental Health Liaison Manager, 2gether.. Background:. There is little guidance on the management of this patient group and little published. . “The College of Emergency Medicine, Best Practice Guideline” (2014) Literature tells us that . # 3 in a 6 part series related to Geriatric Care and Emergency Medicine. Wasn’t she here last week?. Frequent Flyers and other Vexing Tales of the Emergency Department. Optimizing Transitions from the Emergency Department: Transitions/Frequent flyers – Part 1. CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. Geoff Hulten. Why do people Attack Systems?. Crime, espionage. For fun. To make money. Making Money off of Abuse. Driving traffic. Compromising personal information. Compromising computers. Boosting content. : A Hybrid Simulation With A Clinical Outcome. Actual ED . Thoracotomy. Footage. Relevance. :. At . Riverside Methodist Hospital, emergency . thoracotomies. are not an everyday occurrence. Emergency . Outline.. Search process.. Biomedical databases.. Saving your searches.. Managing your results.. Search Process. Building the search.. What is my search question? What am I trying to . answer?. What are . NO 3 May 2013243Western Journal of Emergency MedicineORIGINAL ESEARCHTen Years of Frequent Users in an Urban Emergency Department Gerard B Martin MDStephanie A Stokes-Buzzelli MDJennifer M Peltzer-Jo Insert role and name. What are High Impact Users (HIUs)?. Patients whose use of the Emergency Department has a high impact, either due to:. Having a high incidence of attendance (5 or more attendances per year*). B-ENT, 2014, 10, 87-92 and Throat and Head and Neck Surgery department in the emergency room (ER). Our hospital is a secondary care centre that includes all disciplines. The hospital is a reference ce Abid M. Malik. Meifeng. Lin (PI). Collaborators: Amir . Farbin. (UT) , Jean . Roch. ( CERN). Computer Science and Mathematic Department. Brookhaven National Laboratory (BNL). Distributed ML for HEP.