title: Predictive Modeling for Pressure Ulcers from Intensive Care Unit Electronic Health Records.
abstract: Our goal in this study is to find risk factors associated with Pressure Ulcers (PUs) and to develop predictive models of PU incidence. We focus on Intensive Care Unit (ICU) patients since patients admitted to ICU have shown higher incidence of PUs. The most common PU incidence assessment tool is the Braden scale, which sums up six subscale features. In an ICU setting it's known drawbacks include omission of important risk factors, use of subscale features not significantly associated with PU incidence, and yielding too many false positives. To improve on this, we extract medication and diagnosis features from patient EHRs. Studying Braden, medication, and diagnosis features and combinations thereof, we evaluate six types of predictive models and find that diagnosis features significantly improve the models' predictive power. The best models combine Braden and diagnosis. Finally, we report the top diagnosis features which compared to Braden improve AUC by 10%.
authors: Department of Computer Science and Engineering, The Ohio State University.
authors: Pacharmon
authors: P
authors: Kaewprag
authors: Department of Critical Care Nursing, The Ohio State University Wexner Medical Center.
authors: Cheryl
authors: C
authors: Newton
authors: College of Nursing, The Ohio State University ; Department of Health Services Nursing Education, The Ohio State University Wexner Medical Center.
authors: Brenda
authors: B
authors: Vermillion
authors: Department of Biomedical Informatics, The Ohio State University ; College of Nursing, The Ohio State University.
authors: Sookyung
authors: S
authors: Hyun
authors: Department of Computer Science and Engineering, The Ohio State University ; Department of Biomedical Informatics, The Ohio State University.
authors: Kun
authors: K
authors: Huang
authors: Department of Computer Science and Engineering, The Ohio State University.
authors: Raghu
authors: R
authors: Machiraju
keywords: EHRs; intensive care unit; machine learning; predictive model; pressure ulcers