Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks

BMC Medical Informatics and Decision Making

Research

Sookyung Hyun¹  Brenda Vermillion²  Pacharmon Kaewprag³  Kun Huang⁴  Raghu Machiraju⁴  Cheryl Newton⁵ 

[1] College of Nursing, Pusan National University, Busan, South Korea;College of Nursing, The Ohio State University, Columbus, Ohio, USA;Department of Health Services Nursing Education, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA;Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA;Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA;Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA;Department of Critical Care Nursing, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA;

关键词: Pressure ulcers;    Intensive care units;    Electronic health records;    Bayesian networks;    Model learning;   

DOI  :  10.1186/s12911-017-0471-z

来源: Springer

【 摘 要 】

BackgroundWe develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers.MethodsWe present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features.ResultsFrom the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers.ConclusionsGiven the strong adverse effect of pressure ulcers on patients and the high cost for treating pressure ulcers, our Bayesian network based model provides a novel framework for significantly improving the sensitivity of the prediction model. Thus, when the model is deployed in a clinical setting, the caregivers can suitably respond to conditions likely associated with pressure ulcer incidence.

【 授权许可】

CC BY   
© The Author(s). 2017

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