Intelligent Decision Support to predict patient Barotrauma risk in Intensive Care Units

被引:4
作者
Oliveira, Sergio [1 ]
Portela, Filipe [1 ]
Santos, Manuel Filipe [1 ]
Machado, Jose [1 ]
Abelha, Antonio [1 ]
Silva, Alvaro [2 ]
Rua, Fernando [2 ]
机构
[1] Univ Minho, Algoritmi Res Ctr, P-4719 Braga, Portugal
[2] Ctr Hosp Porto, Intens Care Unit, Oporto, Portugal
来源
CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS/INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT/CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES, CENTERIS/PROJMAN / HCIST 2015 | 2015年 / 64卷
关键词
INTCare; Barotrauma; Intensive Care; Data Mining; Mechanical Ventilation; Decision Support; Probability; Patient-centered; PLATEAU PRESSURE;
D O I
10.1016/j.procs.2015.08.576
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patients was achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: "risk" and "no risk". Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码:626 / 634
页数:9
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