How Information Technologies Support the Intensive Care Systems: An Application of Mortality Prediction Model with Support Vector Machine

Research Article

Austin J Emergency & Crit Care Med. 2015;2(2): 1017.

How Information Technologies Support the Intensive Care Systems: An Application of Mortality Prediction Model with Support Vector Machine

Chien-Lung Chan1, Hsien-Wei Ting1,2 and Chia-Li Chen1,3*

1Department of Information Management, Yuan Ze University, Taiwan

2Department of Neurosurgery, Taipei Hospital, Taiwan

3Department of Information Management, Lung Hwa University, Taiwan

*Corresponding author: Chia-Li Chen, Department of Information Management, Lung Hwa University, No.300, Sec.1, Wanshou Rd., Guishan Shiang, Taoyuan County 33306, Taiwan

Received: January 11, 2015; Accepted: March 06, 2015 Published: March 10, 2015

Abstract

Background and Objective: Intensive care is very important in modern health care. Mortality prediction models are good outcome predictors for intensive care and resources allocation. Many research used the information technologies to construct new mortality prediction models. This study used the Support Vector Machine (SVM) to construct a better mortality prediction model.

Methods: This study collected 695 patients (230 women and 465 men) who were admitted to the surgical intensive care unit in a 600-bed hospital as training data from January 1, 2005 to December31, 2006. Among the 695 patients, 538 (77.41%) patients were alive and 157 were dead (22.59%). This study selected the Gaussian RBF kernel to build a mortality prediction model with empirical data. All variables were included in this model.

Results: The precision rate, recall rate and F-Measure of the SVM model were 0.899, 0.902 and 0.899, respectively. The area under ROC curve (AUR) of models was calculated. The SVM model (AUR=0.932) is better than SAPS II (AUR=0.883) and APACHE II (AUR=0.885) (p<0.01).

Conclusion: The SVM can manage the twin peaks phenomenon which is one of the characteristics of health or medical data.

Keywords: Acute Physiology and Chronic Health Evaluation System, 2nd version (APACHE II); Decision support system; Intensive care; Medical decision making; Mortality prediction; Simplified Acute Physiology System, 2nd version (SAPS II); Support Vector Machine

Introduction

Intensive care is very important in modern health care [1] and the outcome evaluation for intensive care can help to make decisions regarding intensive care facilities [2,3]. Some researchers used mortality prediction models as outcome predictors for intensive care. Popular mortality prediction models include the Acute Physiology and Chronic Health Evaluation System, 2nd version (APACHE II) [4], 3rd version (APACHE III) [5], and 4th version (APACHE IV) [6]; the Simplified Acute Physiology System, 2nd version (SAPS II) [7] and 3rd version [8]; and the Mortality Probability Model, 2nd version (MPM II) [9] and 3rd version (MPM III) [10]. These models are good outcome prediction models for intensive care [11], and are general mortality prediction models for any kind of patient admitted to an intensive care unit. Some mortality prediction models are constructed for a special purpose. For example, the Multiple Organ Dysfunction Score (MODS), Sequential Organ Failure Assessment (SOFA) score and Sepsis-related Organ Failure Assessment are frequently used to assess the outcome of sepsis or multiple organ failure [12-17]. These models are constructed for different purposes.

The first mortality prediction model was constructed by McCabe and Jackson. They collected 173 septicemia patients and divided patients into nonfatal, ultimately fatal and fatal groups. This model may calculate the tendency of mortality [18]. Cullen et al. [19] collected 70 variables of patients and scored these variables from 1 to 4 in terms of severity. They evaluated the severity of patients by summing the collected scores. This is called the Therapeutic Intervention Scoring System. The Glasgow Coma Scale (GCS) is also a severity tendency model [20,21]. Recently, some researchers have simplified the GCS for outcome evaluation, and the simplified models are as good in terms of mortality prediction as the GCS [22]. These models focus on the tendency of mortality, and are pure scoring systems rather than probability systems. Some models constructed based on the probabilities and statistical methodologies. APACHE II and SAPS II are the two most popular models [4,5,7,23]. These models are constructed with Probity regression and use probabilities as the outcome description of mortality. The 2nd version of the Mortality Probability Model (MPM II) is also an intensive care unit (ICU) outcome prediction model with probabilities [9] and new versions of these models have been constructed in recent studies [5,6,8]. Recently, researchers have constructed some mortality prediction models using artificial intelligence technologies [1,17,23-25,50]. These models can add to the health information system as an intensive care facilities decision support system and improve the quality of medical care and facilities allocation.

There are some important characteristics of medical data to consider. One of the most important characteristics is the twin peaks phenomenon. For example, the “within normal range” systolic blood pressure (SBP) is from 90 mmHg to 140 mmHg. We defined hypertension as a SBP greater than 140 mm Hg. Sometimes patients are defined as hypertension if their SBP is less than 90 mmHg [26,27]. Most laboratory or physiological data got normal range of data. It means that the data “within normal range” present that the result of this data is good. Patient with extreme data results will be worse than the patients who have data within normal range. Therefore, if we want to predict the tendency of mortality using these data, the twin peaks phenomenon needs to be solved. APACHE II and SAPS II solved this problem by ranking the raw data and summarizes them into one score as the tendency of mortality or probabilistic models [4,7]. Although many researchers have attempted to improve the accuracy of these mortality models, the definitions of ranking are still constructed subjectively.

Researchers solved medical problems with artificial intelligence technologies. These technologies are usually referred to as classification methodologies. Among the classification methods, the logistic regression (LR) method is one of the most popular methodologies for classification. LR uses a statistic method and builds a logical classification tool. Most studies have used artificial neural networks (ANNs) as the classification method for medical problems, even for mortality prediction in patients in intensive care units [23,28,29]. Muniz et al. [30] evaluated the effect of sub thalamic stimulation in Parkinson disease with probabilistic ANNs, Support Vector Machines (SVMs) and logistic regression models, and concluded that the ANNs are better than other models in this research. The accuracy of ANNs is influenced not only by the numbers of input nodes and the numbers of hidden nodes: large scale data are also required for training models [23]. Unfortunately, the logic of hidden layer cannot be explained.

Decision trees are another kind of classification technology and are more logical in terms of presentation than ANNs. These methods use statistic difference and/or entropy difference as a base to find the best decision nodes and trees [31]. The modeling may be applied to decision support systems and to manage data easily for problemsolving [31,32]. Ting et al. [31] modified the Alvarado scoring system using C5.0 and constructed a better decision model for acute appendicitis diagnosis and improved the misdiagnosis rate. Abu- Hanna et al. [24] combined decision trees with logistic regression and improved the evaluation power of intensive care prognosis. However, the decision tree methods are hard to manage the twin peaks phenomenon, which is one of the characteristics of health or medical data.

Support Vector Machine (SVM) was first proposed by Vapnik in 1995 [33]. It is a kind of information technology which used on the problems of classification. The method uses both statistical learning and structural risk minimization to find an optimal separation hyperplane which can separate different class outcome in a multidimensional space [33,34]. SVM uses both statistical learning and structural risk minimization (SRM) to find an optimal separation hyperplane which can separate different class outcome in a multidimensional space [34-36]. It has been used on many problems in different fields, included text categorization; image recognition; face detection; voice recognition; genetic classification and medical diagnostic problems [34,37-40]. Zhu et al. [41] constructed a SVMbased classifiers. It may has better performance to evaluate the pulmonary nodules found in computer tomography are malignant or not. Yamamoto et al. [40] also used the SVM technology to identify the possible multiple sclerosis lesions correctly in the brain magnetic resonance images. Verplancke et al. [25] constructed a novel mortality prediction model for hematological malignancies patients with SVM, which was better than logistic regression. It is good method for classification and they improved many classification problems of medical fields.

Based on the fitness of kernels distributions, the SVM is one of the classification models that can be used to manage this problem. A new mortality prediction model using SVM technology was constructed for the patients who are admitted to ICU in this study.

Methodology

SVM model proposed by Vapnik [33] is an effective classification method and used in many different fields, including text categorization; image recognition; face detection; voice recognition; genetic classification and medical diagnostic problems [34,37-40]. It uses both statistical learning and structural risk minimization (SRM) to find an optimal separation hyperplane, which can separate different class outcomes in a multi-dimensional space [34-36]. Proper parameter selection can improve the classification accuracy of the SVM model. We describe some concepts of support vector machines below. Given training data xi ,i =1,..., n yi∈{1,i1} the SVM requires the solution of the following optimization problem [42]: min w,b,ξ           1 2 W T W+c i=1 l ξ i subject to   y i ( W T ϕ( X i )+b )1 ξ i , MathType@MTEF@5@5@+=feaaguart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLnhiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=xfr=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@7002@ ξi are slack variables used to tolerate the classification condition which cannot separate linearly (Figure 1).