| Title |
Towards Personalized Intensive Care Decision Support Using a Bayesian Network: A Multicenter Glycemic Control Study |
| Authors |
Asma Abu-Samah(Asma Abu-Samah) ; Normy Norfiza Abdul Razak(Normy Norfiza Abdul Razak) ; Fatanah Mohamad Suhaimi(Fatanah Mohamad Suhaimi) ; Ummu Kulthum Jamaludin(Ummu Kulthum Jamaludin) ; James Geoffrey Chase(James Geoffrey Chase) |
| DOI |
https://doi.org/10.5573/IEIESPC.2019.8.3.202 |
| Keywords |
Personalized glycemic control ; Intensive care units ; Bayesian network ; Discretization technique ; performance prediction |
| Abstract |
Personalized treatment in glycemic control (GC) is a visibly promising research area that requires improved mechanisms providing patient-specific procedures to enable complicated decision support. Available per-patient data must be more than written records, and be fully integrated in this personalization process. This article presents a process for relating the intensive care unit patients’ demographic and admission data to their GC performance. With this objective, a probabilistic Bayesian network was chosen to provide more personalized decisions. As a case study, average daily blood glucose measurements were chosen as the interest target node in order to weigh GC that provides a reduced nursing workload. To test the idea, data from 482 patients, with nine variables from four Malaysian intensive care units with different controls were exploited. The identified steps crucial in building a dependable model are variable selection, continuous state discretization, and unsupervised structure learning. Using a multi-target node evaluation, a network with 80% mean overall classification precision was obtained with a normalized equal distance discretization algorithm and a maximum weight spanning tree technique. Meanwhile, the interest target node scored 90.39% precision. The results from this study, which are complemented with an evaluation of missing data, are proposed as a benchmark for using Bayesian networks in this type of application. |