title: explICU: A web-based visualization and predictive modeling toolkit for mortality in intensive care patients.
abstract: Preventing mortality in intensive care units (ICUs) has been a top priority in American hospitals. Predictive modeling has been shown to be effective in prediction of mortality based upon data from patients' past medical histories from electronic health records (EHRs). Furthermore, visualization of timeline events is imperative in the ICU setting in order to quickly identify trends in patient histories that may lead to mortality. With the increasing adoption of EHRs, a wealth of medical data is becoming increasingly available for secondary uses such as data exploration and predictive modeling. While data exploration and predictive modeling are useful for finding risk factors in ICU patients, the process is time consuming and requires a high level of computer programming ability. We propose explICU, a web service that hosts EHR data, displays timelines of patient events based upon user-specified preferences, performs predictive modeling in the back end, and displays results to the user via intuitive, interactive visualizations.
authors.forename: Robert
authors.initials: R
authors.lastname: Chen
authors.forename: Vikas
authors.initials: V
authors.lastname: Kumar
authors.forename: Natalie
authors.initials: N
authors.lastname: Fitch
authors.forename: Jitesh
authors.initials: J
authors.lastname: Jagadish
authors.lastname: Lifan Zhang
authors.forename: William
authors.initials: W
authors.lastname: Dunn
authors.lastname: Duen Horng Chau
mesh_terms: D019540:Area Under Curve; D003422:Critical Care; D003936:Diagnosis, Computer-Assisted; D057286:Electronic Health Records; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D020407:Internet; D016015:Logistic Models; D000069550:Machine Learning; D015999:Multivariate Analysis; D012372:ROC Curve