A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries
- Soo-Yeon Ji^{1}Email author,
- Rebecca Smith^{1},
- Toan Huynh^{2} and
- Kayvan Najarian^{1}
DOI: 10.1186/1472-6947-9-2
© Ji et al; licensee BioMed Central Ltd. 2009
Received: 01 February 2008
Accepted: 14 January 2009
Published: 14 January 2009
Abstract
Background
This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.
Methods
Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.
Results
For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.
Conclusion
This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.
Background
According to a 2001 National Vital Statistics Report [1], nearly 115,200 deaths occur each year due to traumatic injury, and many patients who survive suffer life-long disabilities. Among all causes of death and permanent disability, traumatic brain injury (TBI) is the most prevalent. Of the 29,000 children who are hospitalized each year with TBI, a significant percentage will suffer from neurological impairment [2]. It has also been reported that the traumatic brain injuries are the most expensive affliction in the United States, with an estimated cost of $224 billion [3].
Computer-aided systems can significantly improve trauma decision making and resource allocation. Since trauma injuries have specific causes, all with established methods of treatment, fatal complications and long-term disabilities can be reduced by making less subjective and more accurate decisions in trauma units [4]. In addition, it has been suggested that an inclusive trauma system with an emphasis on computer-aided resource utilization and decision making may significantly reduce the cost of trauma care [1].
Since the treatment of traumatic brain injuries is extremely time-sensitive, optimal and prompt decisions during the course of treatment can increase the likelihood of patient survival [5, 6]. It is also believed that the predicted length of stay in the ICU is an important factor when deciding on the patient transport method (i.e. ambulance or helicopter), as more critical patients are expected to spend more time in the ICU, and these stand to benefit the most from helicopter transport. Studies have emphasized the critical impact of helicopter transport on trauma mortality rates, since the speed of ambulance transport is limited by road and weather conditions, and may also be constrained by traffic congestion. However, it is difficult to compare ground and helicopter transportation and the corresponding care provided to the patients [7]. Cunningham [8] attempts a comparison based on the outcome of the treatment given to trauma patients. Based on his study, patients in critical condition are more likely to survive if transported via helicopter. However, the high cost of helicopter transport remains a major problem [9, 10]. In recent studies, Gearhart evaluated the cost-effectiveness of helicopter for trauma patients and suggested that on average the helicopter transport cost is about $2,214 per patient, and $15,883 for each additional survivor [11]. Eventually, the cost is almost $61,000 per surviving trauma patient. Eckstein [12] states that 33% of patients who are transported by helicopter are discharged home from the emergency department [12], rather than being sent to ICU. This indicates that a significant number of trauma patients transported by helicopter actually have relatively minor injuries. This emphasizes the necessity of a comprehensive transport policy based on patient condition and predicted outcome.
Several computer-assisted systems already exist for decision-making in trauma medicine. The majority of these systems [13, 14] are designed to perform a statistical survey of similar cases in trauma databases, based only on patient demographics. As such, they may not be sufficiently accurate and/or specific for practical implementation. Other medical decision making systems employ the predictive capabilities of artificial neural networks [15–17]; however, due to the 'black box' nature of these systems, the reasoning behind the predictions and recommended decisions is obscured. Currently, none of these existing systems are in widespread use in trauma centers. There are three main reasons: the use of non-transparent methods, such as neural networks; the lack of a comprehensive database integrating all relevant available patient information for specific prediction processes; and poor performance due to the exclusion of relevant attributes and the inclusion of those irrelevant to the current task, resulting in rules that are too complicated to be clinically meaningful.
Several machine learning algorithms are commonly applied to medical applications. These include support vector machines (SVM), and decision tree algorithms such as Classification and Regression Trees (CART) and C4.5. Boosting is also employed for improving classification accuracy. However, despite the relatively successful performance of these algorithms in medical applications, they have limited success in separating and identifying important variables in applications where there are a large number of available attributes. This suggests that combining machine learning with a method to identify the most uncorrelated set of attributes can increase our understanding of the patterns in medical data and thus create more reliable rules. The literature of biomedical informatics reinforces the benefits of this approach. Andrews et al. [18] use decision tree (DT) and logistic regression (LR) methods to identify the commonalities and differences in medical database variables. Kuhnert [19] emphasises that non-parametric methods, such as CART and multivariate adaptive regression splines, can provide more informative models. Signorini et al. [20] design a simple model containing variables such as age and GCS, but the small number of attributes may limit the reliability of the generated rules. Guo [21] finds that CART is more effective when combined with the logistic model, and Hasford [22] compares CART and logistic regression, and finds that CART is more successful in outcome prediction than logistic regression alone.
Therefore, a possible approach to create accurate and reliable rules for decision making is to combine machine learning and statistical techniques [23, 24]. This paper analyzes the performance of several combinations of machine learning algorithms and logistic regression, specifically in the extraction of significant variables and the generation of reliable predictions. Though a transparent rule-based system is preferable, other methods (such as neural networks) are also tested in the interest of comparision. A computational model is developed to predict final outcome (home or rehab and alive or dead) and ICU length of stay. In addition, we identify the factors and attributes that most affect decision making in the treatment of traumatic injury.
Our hypotheses are as follows:
1. We hypothesize that a rule-based system, attractive to physicians as the reasoning behind the rules is transparent and easy to understand, can be as accurate as "black-box" methods such as neural networks and SVM.
2. We hypothesize that when trained correctly, a computer-aided decision making system can provide clinically useful rules with a high degree of accuracy.
3. Studies mentioned earlier have examined which variables are most significant in the recommendation/prediction making process. We hypothesize that airway status, age, and pre-existing conditions such as myocardial infarction and coagulopathy are significant variables.
Methods
Rules are created by processing patterns discovered in the traumatic brain injury (TBI) datasets. More specifically, they are generated by analyzing the logical and grammatical relationships among the input features and the resulting outcomes. Rules are formally defined as grammatical expressions of knowledge extracted using specific logical operations on the available features [6].
CART and C4.5 are among the most popular algorithms for creating reliable rules, but they are limited in their ability to identify the most significant variables. We therefore perform statistical analysis using logistic regression, which is typically effective in discovering statistically significant regression coefficients [24]. Although stepwise regression is designed to find significant variables, it may not perform well with CART when dealing with small scale datasets [25]. Therefore, in this paper, logistic regression with direct maximum likelihood estimation (Direct MLE) is used.
Dataset
Three different datasets are used in the study: on-site, off-site, and helicopter. The on-site dataset contains data captured at the site of the accident; the off-site dataset is formed at the hospital after patients are admitted; and the helicopter dataset consists of the records for patients who are transported to hospital by helicopter. The on and off-site datasets are used to predict patient survival (dead/alive) and final outcome (home/rehab), and the helicopter dataset is used to predict ICU length of stay, which is a measure used in estimating the need for helicopter transportation. The datasets are provided to us by the Carolinas Healthcare System (CHS) and the National Trauma Data Bank (NTDB).
On-site dataset
On-site dataset
Variable | Possible Values |
---|---|
Gender* | 2 (Male, Female) |
Blunt* | Blunt, Penetrating |
ChiefComp* | MVC, Fall, Pedestrian, Motorcycle Crash, etc |
Position* | Passenger, Driver, Cyclist, Motorcycle Passenger, etc |
Age | Patient's age |
FSBP (Initial Blood Pressure) | 0 ≤ FSBP ≤ 300 |
GCS (Glasgow Coma Score) | 3 ≤ GCS ≤ 15 |
ISS (Total Injury Severity Score) | 0 ≤ ISS ≤ 75 |
Pulse | 0 ≤ Pulse ≤ 230 |
Respiration Rate | 0 ≤ Respiration ≤ 68 |
Off-site dataset
The off-site dataset contains information on comorbidities and complications, and includes all variables. A total of 1589 cases are included in the database: 588 fatal and 1001 non-fatal. The inputs include both categorical and numerical attributes. The predicted outcomes are defined as the patients' survival, i.e. alive or dead, and the exact outcome for surviving patients, i.e. rehab or home.
Off-site dataset
Variable | Alive | Dead | Rehab | Home |
---|---|---|---|---|
Cases | 1001 | 588 | 628 | 213 |
Male* | 704 (70.3%) | 404 (68.7%) | 443 (70.5%) | 150 (70.4%) |
Female* | 297 (29.7%) | 184 (31.3%) | 185 (29.5%) | 63 (29.6%) |
Age | 41.2 ± 19.6 | 49.2 ± 24.1 | 39.6 ± 19.3 | 37.2 ± 16.6 |
FSBP | 126 ± 33.4 | 119.3 ± 45.6 | 125.3 ± 31.6 | 124.5 ± 34.1 |
FURR | 15.3 ± 10.9 | 13.9 ± 11.9 | 14.4 ± 11.1 | 18.2 ± 10.5 |
GCS | 8.7 ± 5.3 | 27.5 ± 5.2 | 7.9 ± 5.2 | 10.5 ± 5.1 |
ISS | 30.5 ± 12.8 | 35.3 ± 14.7 | 32 ± 13.2 | 27.1 ± 11.7 |
EDEYE | 2.4 ± 1.4 | 2.1 ± 1.4 | 2.2 ± 1.4 | 2.8 ± 1.4 |
ED Verbal | 2.7 ± 1.8 | 2.3 ± 1.7 | 2.4 ± 1.8 | 3.3 ± 1.8 |
EDRT | 4.6 ± 3.2 | 3.8 ± 3.3 | 4.1 ± 3.3 | 5.7 ± 2.89 |
Head AIS | 3.0 ± 1.6 | 3.6 ± 1.6 | 3.1 ± 1.8 | 2.5 ± 1.4 |
Thorax AIS | 2.3 ± 1.7 | 2.4 ± 1.8 | 2.3 ± 1.8 | 2.4 ± 1.7 |
Abdomen AIS | 1.1 ± 1.5 | 1.1 ± 1.6 | 1.0 ± 1.5 | 1.5 ± 1.7 |
Intubation* | Yes/No | |||
Prexcomor* | 17 values: Acquired Coagulopathy, Chronic Alcohol Abuse, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, Coronary Artery Disease, Coumadin Therapy, Documented History of Cirrhosis, Gastric or Esophageal Varices, Hypertension, Insulin Dependent, Myocardial Infarction, Non-Insulin Dependent, Obesity, Pre-existing Anemia, Routine Steroid Use, Serum Creatinine > 2 mg % (on Admission), Spinal Cord Injury | |||
Complications* | Acute Respiratory Distress Syndrome (ARDS), Aspiration Pneumonia, Bacteremia, Coagulopathy, Intra-Abdominal Abscess, Pneumonia, Pulmonary Embolus | |||
Safety* | Seat Belt, None Used, Air Bag Deployed, Helmet, Other, Infant/Child Car Seat, Protective Clothing |
Helicopter dataset
Helicopter dataset
Variable | Severe (ICU stay > 2 days) | Non-Severe (ICU stay ≤ 2 days) |
---|---|---|
Cases | 301 | 196 |
Male | 201 (66.8%) | 132 (67.3%) |
Female | 100 (33.2%) | 64 (32.7%) |
Age | 30.6 ± 16.6 | 32.9 ± 17.2 |
FSBP | 137.7 ± 23.2 | 127.6 ± 28.0 |
GCS | 11.7 ± 4.87 | 6.47 ± 5.01 |
ISS | 14.2 ± 8.1 | 23.7 ± 9.47 |
Pulse | 101.4 ± 22.3 | 108.2 ± 26.6 |
Resp. Rate | 15.6 ± 9.44 | 6.45 ± 10.6 |
ISS-HN | 2.83 ± 0.86 | 3.46 ± 0.91 |
Learning algorithms
It is known that the patterns observed in trauma cases are often extremely complicated; that is, the treatment outcomes for two apparently similar patients may turn out to be significantly different. Linear methods have proven insufficient even in the analysis of patterns as simple as the "exclusive-or" function. Because these limitations are inherited by linear regression methods, the use of non-linear techniques for computer-aided trauma systems has been broadly encouraged [27]. Neural networks are a common choice; however, they are not transparent, since the knowledge learned from the training examples is hidden within the structure and weights of the networks [28]. While there are existing methods that can extract approximate rules to represent this hidden knowledge, they cannot truly represent the trained networks [6]. Support Vector Machines (SVM's) and AdaBoost share the same problem: the knowledge used in the decision making process is not visible to humans, a requirement that is extremely important in medical applications. Rule-based methods such as CART and C4.5 provide completely transparent computational decision making systems while still utilizing some nonlinear capabilities. Considering the importance of decision transparency in medical informatics, we use CART and C4.5 as the main algorithms for rule extraction.
Classification and Regression Tree (CART)
CART, designed by L. Breiman [29], applies information-theoretic concepts to create a decision tree. This allows for the capture of rather complex patterns in data, and their expression in the form of transparent grammatical rules [30]. CART's nonlinear extensions are still widely used in data mining and machine learning, due to the algorithm's efficiency in dealing with multiple data types [31] and missing data. In the latter case, CART simply uses a substitution value, defined as a pattern similar to the best split value in the node [29]. In addition, CART supports an exhaustive search of all variables and split values to find the optimal splitting rules for each node. The splitting stops at the pure node containing fewest examples.
C4.5
where p(k _{ i }, S) is the relative frequency of examples in S that belong to class k _{ i }. The best split is the one that most reduces this value. The output of the algorithm is a decision tree, which can be easily represented as a set of symbolic IF-THEN rules.
Adaptive Boost (AdaBoost)
AdaBoost, introduced by Freund and Schapire [35], is an algorithm that constructs a robust classifier as a linear combination of weak classifiers. Adaboost repeatedly calls a given weak learning algorithm in a set of rounds t = 1, ..., T. A distribution of weights is maintained over the training set, such that D _{ t }(k) is the distribution's weight for training example k on round t. The aim of the weak learner is to find a good weak hypothesis h _{ t }: X → {-1, +1} for the distribution D _{ t }, where goodness is measured by the error of the hypothesis with respect to D _{ t }. Then D _{ t }is updated such that incorrectly classified examples have their weights increased, forcing the weak classifier to concentrate on the more difficult training examples. Correspondingly, correctly classified examples are given less weight. Adaboost selects some parameter α _{ t }to denote the importance of h _{ t }, and after all rounds are complete, the final hypothesis H is a weighted majority vote of all T weak hypotheses. It has been shown that as with other boosting algorithms, if each weak hypothesis is at least slightly better than random, then the training error falls at an exponential rate. However, Adaboost is also able to adapt to the error rates of individual weak hypotheses, so each subsequent classifier is adjusted in favor of examples mislabelled by previous classifiers [36].
Support Vector Machine (SVM)
where b is a real number (bias term) and w and F have the same dimensionality. For an unknown input vector x _{ j }, classification means finding:
f(x _{ j }) = sgn(y _{ i }<w, φ(x _{ i }) > -b)
It can be shown that this minimum occurs when w = Σ_{ i } α _{ i } γ _{ i } φ(x _{ i }), where α _{ i }is a positive real number that represents the strength of training point x _{ i }in the final classification decision. The subset of points where ai is non-zero consists of the points closest to the hyperplane, and these are the support vectors. Since SVM is able to handle large feature spaces, it is frequently used in many real world problems even though it is computationally expensive [39].
Neural networks
where σ indicates the neuron radius [40, 41]. RBFs utilize the distance in feature space to calculate the weight for each neuron.
Pre-processing
The datasets contain nominal categorical variables, such as gender and complication type. Gender is replaced by a binary variable (0 for male, 1 for female). Every nominal value is dummy-coded (Yes/No to 1/0) and treated as an individual attribute. Ten fold cross-validation is used to measure the generalization quality and scalability of the rules. Each dataset is divided into ten mutually exclusive subsets [42], and in each stage nine are used for training and one is used for testing. Ten different trees are therefore formed for each dataset.
Rule performance metrics
Once a variety of rules are generated, the performance of each rule is measured as the probability of correct prediction. Assume that D is a dataset including the instance (x _{ i }, y _{ i }), where y _{ i }is the real survival outcome. Let D _{ r }be the training set, and a subset D _{ t }∈ (D\D _{ r }) be used for testing. The performance of the rule is calculated as:
acc _{ R }= prob(y _{ i }= y ^{ R }|(x _{ i }, y _{ i }) ∈ D _{ t })
where TP, TN, FP, and FN are defined as before. In this application, high sensitivity is more important than high specificity. When patient lives are at stake – for example, in the choice of transportation – false positives are preferable to false negatives, even if they incur greater financial cost.
Improving rule quality
where X _{ i }are variables with numeric values, Y is the outcome (dichotomous; 0 or 1, e.g. Alive/Dead), and the β's are the regression coefficients that quantify the contributions of the numeric variables to the overall probability [22].
Logistic regression provides knowledge of the relationships and strengths among the multiple independent variables and the response variable. It does not assume any distribution on the independent variables; they do not have to be normally distributed, linearly related or of equal variance within each group. The most important interpretation from logistic regression is the odds ratio, which measures the strength of the partial relationship between an individual predictor and the outcome event [43].
If the plots of the residuals versus the predictors do show curvature, a quadratic term should be tested for statistical significance for suggesting better model. If the coefficient for this quadratic term is significant, the quadratic term should be included. Even though our model does not show any strong curvature, we test the Head AIS variable using a quadratic term, to validate our results. The model is as follows:
logit = α + βx + γx ^{2}
where α is an intercept term, β is a parameter of the predictor, and γ is a parameter of squared predictor. For the Head AIS variable, the estimate of β is -0.1820 (p value = 0.0015), and the estimate of γ is -0.0124 (p value = 0.2058). These p values indicate that Head AIS does not require a quadratic term; therefore, there is a linear relationship between the logit and its predictor.
To test the significance of the individual variables, we compare a reduced model that drops one of the independent variables with a full model using log likelihood test. The likelihood ratio test itself does not tell us if any particular independent variables are more important than others. However, by estimating the maximum likelihood, we can analyze the difference between results for the full model and results for a nested reduced model which drops one of the independent variables. A non-significant difference indicates no effect on performance of the model, hence we can justify dropping the given variable. We call this directed MLE.
If the chi-square value for this test is significant, the variable is considered to be a significant predictor. Following these tests, only the significant variables (p value <= .05) are selected.
Note that forward and stepwise model selections are also available to discover the significance of individual attributes [19, 25]. In the literature of statistical regression, the stepwise method is commonly used to find the best subset of variables for outcome prediction, considering all possible combinations of variables. However, the stepwise approach may not guarantee that the most significant variables are selected due to the repetition of insertion and deletion. For example, age may not be selected as important variable; however, physicians may believe that patient age is important in deciding treatment. Therefore, we prefer to use directed MLE for our medical application. Our other justification for using MLE is empirical; in our previous study [10], we found that the direct MLE method has slightly higher accuracy in finding significant variables than stepwise and forward model selection. A statistical analysis tool, in this case SAS, is used to calculate the significance of individual attributes.
Constructing reliable rules
As mentioned previously, SVM and neural networks do not directly produce grammatical rules; therefore, only CART and C4.5 are considered for rule extraction. Those variables identified as significant are used as input variables to CART and C4.5. Also, if a rule is created only to accommodate one or two examples, it may be too specific to be applied to the entire population. Consequently, only the rules with both high accuracy and a sufficiently large number of supporting examples are used to form the rule base. Note that SVM, Neural Networks and AdaBoost are still tested in the interests of performance comparision, even though they do not generate rules. These algorithms are in widespread use, and comparing them to the rule based CART and C4.5 algorithms tests and validates the accuracy and stability of the rule-based system.
Results
The average accuracy of survival prediction without any knowledge of pre-existing conditions is 73.9%, rising to 75.8% when this knowledge is included. The off-site dataset is therefore used for further prediction tests, as it contains records of pre-existing conditions. We discovered that knowledge of these conditions appears at the highest level of the tree when using CART and C4.5, indicating their potential importance in the decision-making process. In particular, coagulopathy (bleeding disorder), which can result in severe haemorrhage, may be among the most important factors to consider in patients with TBI.
Due to the transparent nature of the rule-based system used in this study, the generated rules can not only help trauma experts predict the likelihood of survival, but also provide the reasoning behind these predictions in order to help physicians better allocate their resources.
Since the total number of examples used for training is rather small, initially only rules with at least 85% prediction accuracy on the testing sets are included in the rule base. This threshold was chosen following discussion with trauma experts. However, we also incorporate rules with accuracy between 75% and 85%. There are two reasons for this. Firstly, the accuracy of a rule may be low due to the lack of of a truly complete database, rather than a flaw in the rule itself. Secondly, even though a rule may have low accuracy, it might include knowledge of hidden relationships between variables. For example, most trauma experts consulted believed that a patient with an ISS score over 25 would have little chance of survival. However, the survival probability might be higher for a patient with a high ISS score, but lower head and thorax AIS score, provided appropriate and prompt treatment is provided. Therefore, we will use those rules with accuracy between 75% and 85% as additional "supporting rules" in suggesting possible treatment. This issue is addressed further in the discussion section.
Significant variable selection
Significant variables of off-site dataset
Variable | Coefficient | Walds χ^{2} | P-value | Odd Ratios | Mean ± S.D. |
---|---|---|---|---|---|
AIS Head | -0.58 | 23.61 | <.0001 | 0.56 | 3.25 ± 1.64 |
AIS Thorax | -0.13 | 4.37 | 0.003 | 0.88 | 2.33 ± 1.78 |
ID* | 1.27 | 5.70 | 0.02 | 3.55 | - |
MI* | 1.43 | 19.44 | <.0001 | 4.18 | - |
ARDS* | 0.98 | 20.24 | <.0001 | 2.66 | - |
Cg* | 0.63 | 24.96 | <.0001 | 1.88 | - |
Age | -0.03 | 29.22 | <.0001 | 1.03 | 44.15 ± 21.70 |
EDRTS | -0.27 | 4.94 | 0.03 | 0.77 | 12.10 ± 16.03 |
ISS | 0.02 | 6.06 | 0.01 | 1.02 | 15.82 ± 19.03 |
Significant Variables of Helicopter dataset
Variable | Coefficient | Wals χ^{2} | P-value | Odd Ratios | Mean ± S.D. |
---|---|---|---|---|---|
Age | -0.02 | 3.17 | <.0001 | 0.98 | 31.79 ± 17.50 |
Blood Pressure | 0.01 | 2.85 | 0.01 | 0.01 | 129.45 ± 30.51 |
ISS-HN | 0.01 | 0.003 | 0.25 | 1.11 | 3.22 ± 1.00 |
ISS | -0.14 | 36.47 | 0.02 | 0.87 | 19.56 ± 11.09 |
In this study the scale of the data is small and several variables are unknown, so participating physicians assisted in identifying significant variables. These physicians selected age, GCS, blood pressure, pulse rate, respiration rate, and airway as important factors.
Measuring performance
Performance comparison of five machine learning methods
Logistic | AdaBoost | C4.5 | CART | SVM | RBF NN | |
---|---|---|---|---|---|---|
All Variables | 69.4% | 70% | 68% | 75.6% | 73% | 67.2% |
Significant Vars. only | 72.9% | 73% | 75.2% | 77.6% | 79% | 79.04% |
Prediction results for outcome and ICU days
Logistic | AdaBoost | C4.5 | CART | SVM | RBF NN | |
---|---|---|---|---|---|---|
Exact Outcome | 74.6% | 73% | 75.6% | 72% | 72.6% | 72.8% |
Days in ICU | 80.6% | 78.7% | 77.1% | 77.4% | 80.1% | 77.4% |
Performance comparison of AUC in ROC curve analysis
Logistic | AdaBoost | C4.5 | CART | SVM | |
---|---|---|---|---|---|
All Variables | 63.7% | 63.1% | 58.1% | 60% | 64.5% |
Significant Vars. only | 66.9% | 67.5% | 63.2% | 64.6% | 67.6% |
ROC performance in Exact outcome and ICU days predictions
Variable | Logistic | AdaBoost | C4.5 | CART | SVM |
---|---|---|---|---|---|
Exact outcome | 76.8% | 76.4% | 71.9% | 71.5% | 68.7% |
Days in ICU | 79.2% | 74.6% | 76.6% | 73% | 71.9% |
Constructed database using CART and C4.5
Numerous rules were generated with the CART and C4.5 rule extraction algorithm. Following discussion with trauma experts, we identified the robust rules as those with over 85% accuracy. For survival prediction, the average rule accuracy using all available variables is 82%, and 83.9% when using only the most significant variables.
Extracted reliable rules for survival prediction (> 85% accuracy)
Rules | Test Accuracy | Method |
---|---|---|
(Cg = 'Yes') and HEAD < 2 and AGE < 76.65 Then Alive | 29/34(85.3%) | CART |
(Cg = 'No') and (MI = 'No') and AGE < 61.70 and HEAD ≤ 4 and (ARDS = 'No') Then Alive | 334/375(89.1%) | CART |
(Cg = 'No') and (MI = 'No') and HEAD ≥ 5 and AGE < 22.35 Then Alive | 55/64(85.9%) | CART |
ISS ≥ 28 and (Cg = 'No') and THORAX ≤ 4 and 62.25 ≤ AGE < 69.00 and EDRTS ≥ 2.88 Then Alive | 10/11(90.9%) | CART |
ISS ≥ 23 and (Cg = 'No') and THORAX ≤ 4 and 69 ≤ AGE < 72.35 Then Alive | 13/15(86.7%) | CART |
HEAD ≤ 2 and (MI = 'No') and (Cg = 'No') and AGE ≤ 62 Then Alive | 182/206(88.3%) | C4.5 |
(MI = 'Yes') and AGE ≤ 62 and EDRTS > 5.39 and ISS ≤ 25 Then Alive | 19/20(95%) | C4.5 |
THORAX > 3 and HEAD ≤ 4 and (ARDS = 'No') and AGE ≤ 62 Then Alive | 126/148(85.1%) | C4.5 |
THORAX ≤ 2 and EDRTS ≤ 0.87 and ISS > 38 Then Dead | 12/13(92.3%) | C4.5 |
(MI = 'Yes') and AGE > 82.6 Then Dead | 16/18(88.9%) | C4.5 |
(MI = 'Yes') and ISS > 30 Then Dead | 45/50(90%) | C4.5 |
HEAD > 4 and (MI = 'Yes') Then Dead | 25/27(92.6%) | C4.5 |
(Cg = 'Yes') and HEAD ≤ 4 and AGE > 78 Then Dead | 12/14(85.7%) | C4.5 |
(ID = 'Yes') and AGE > 78 and (MI = 'Yes') and HEAD ≤ 4 Then Dead | 27/31(87.1%) | C4.5 |
HEAD > 0 and HEAD ≤ 2 and (ID = 'Yes') and (ARDS = 'No') and AGE ≤ 75.2 Then Alive | 107/118(90.7%) | C4.5 |
(ID = 'Yes') and (MI = 'Yes') and HEAD > 3 Then Dead | 43/49(87.8%) | C4.5 |
(MI = 'Yes') and (ID = 'Yes') and AGE > 78 Then Dead | 32/37(86.5%) | C4.5 |
HEAD > 4 and (MI = 'Yes') Then Dead | 25/27(92.6%) | C4.5 |
(MI = 'Yes') and ISS > 30 Then Dead | 45/50(90%) | C4.5 |
(MI = 'Yes') and AGE > 79.6 and ISS > 12 Then Dead | 27/30(90%) | C4.5 |
(Cg = 'Yes') and HEAD ≤ 4 and AGE > 79.6 Then Dead | 12/14(85.7%) | C4.5 |
(ARDS = 'No') and (MI = 'No') and (Cg = 'No') and HEAD ≤ 4 and AGE ≤ 62 Then Alive | 335/376(89.1%) | C4.5 |
(MI = 'Yes') and (ID = 'Yes') and AGE > 78 Then Dead | 15/16(93.8%) | C4.5 |
(MI = 'Yes') and HEAD ≤ 4 and ISS > 38 Then Dead | 29/34(85.3%) | C4.5 |
(MI = 'Yes') and AGE ≤ 61.6 and ISS > 27 Then Dead | 26/30(86.7%) | C4.5 |
HEAD = 2 and (MI = 'No') and AGE ≤ 62 and ISS ≤ 38 Then Alive | 235/270(87%) | C4.5 |
THORAX > 0 and (ID = 'Yes') and ISS ≤ 30 Then Alive | 13/14(92.9%) | C4.5 |
Extracted supporting rules for survival prediction (75% – 85% accuracy)
Rules | Test Acc. | Method |
---|---|---|
(Cg = 'Yes') and 2.5 ≤ HEAD < 3.5 and EDRTS < 6.07 and 35.65 ≤ AGE < 55.25 Then Alive | 10/12(83.3%) | CART |
(Cg = 'Yes') and HEAD ≥ 3 and EDRTS ≥ 6.07 and THORAX < 1 Then Alive | 33/43 (76.7%) | CART |
(Cg = 'No') and (MI = 'No') and AGE < 61.70 and (ARDS = 'Yes') and HEAD < 3 Then Alive | 50/59(84.7%) | CART |
(Cg = 'No') and (MI = 'No') and ISS = 24 and 61.70 = AGE < 68.90 and HEAD ≤ 3 Then Dead | 11/13(84.6%) | CART |
AGE < 61.70 and HEAD ≤ 4 and (MI = 'No') Then Alive | 625/793(78.8%) | CART |
HEAD ≥ 5 and (Cg = 'No') and AGE < 22.85 Then Alive | 60/73(82.2%) | CART |
HEAD ≥ 5 and (Cg = 'No') and EDRTS < 5.02 and 22.85 ≤ AGE < 28 and ISS ≥ 33 Then Dead | 11/13(84.6%) | CART |
ISS ≥ 23 and (ID = 'Yes') and 61.70 ≤ AGE < 80.50 and (ARDS = 'No') and (Cg = 'Yes') Then Dead | 12/15(80.0%) | CART |
ISS ≥ 23 and (ID = 'Yes') and AGE ≥ 80.50 Then Dead | 42/51(82.4%) | CART |
AGE < 61.70 and (Cg = 'Yes') and HEAD ≤ 3 and ISS < 42 Then Alive | 47/56(83.9%) | CART |
AGE < 61.70 and (Cg = 'No') and (MI = 'No') Then Alive | 559/706 (79.2%) | CART |
(MI = 'No') and (Cg = 'Yes') and HEAD ≤ 3 and AGE < 60.40 and ISS < 42 Then Alive | 47/56(83.9%) | CART |
(MI = 'No') and (Cg = 'No') and ISS ≤ 23 and EDRTS < 6.07 Then Alive | 578/728(79.4%) | CART |
AGE < 62 and HEAD ≤ 4 and ISS ≤ 25 Then Alive | 648/822(78.8%) | CART |
HEAD ≥ 5 and (Cg = 'No') and AGE < 22.85 Then Alive | 60/73(82.2%) | CART |
ISS ≥ 23 and AGE ≥ 80.50 Then Dead | 45/55(81.8%) | CART |
AGE < 61.70 and HEAD ≤ 4 and (MI = 'No') Then Alive | 625/793(78.8%) | CART |
AGE < 61.60 and (MI = 'No') and ISS < 42 and (Cg = 'Yes') and HEAD ≤ 3 Then Alive | 47/56(83.9%) | CART |
AGE < 61.60 and HEAD ≤ 4 and (MI = 'No') and (Cg = 'No') Then Alive | 421/503(83.7%) | CART |
AGE ≥ 61.60 and ISS ≥ 23 and (Cg = 'Yes') Then Dead | 44/54(81.5%) | CART |
AGE < 61.70 and HEAD ≤ 4 and (ISS < 27) Then Alive | 646/820(78.8%) | CART |
HEAD ≥ 5 and (Cg = 'No') and 22.35 ≤ AGE < 25.85 and (MI = 'No') and ISS ≥ 30 Then Dead | 10/13(76.9%) | CART |
ISS ≥ 23 and 61.70 ≤ AGE < 74.10 and EDRTS < 2.88 Then Dead | 21/28(75.0%) | CART |
ISS ≥ 23 and AGE ≥ 74.10 Then Dead | 92/119(77.3%) | CART |
(MI = 'No') and HEAD ≤ 4 and AGE ≤ 62 ISS ≤ 38 Then Alive | 508/612(83%) | C4.5 |
3 < HEAD ≤ 4 and (MI = 'No') and (ID = 'Yes') and (Cg = 'No') and ISS ≤ 59 Then Alive | 138/175(78.9%) | C4.5 |
HEAD > 1 and (MI = 'Yes') and ISS > 22 Then Dead | 59/70(84.3%) | C4.5 |
HEAD > 3 and (MI = 'Yes') Then Dead | 49/58(84.5%) | C4.5 |
(Cg = 'No') and (MI = 'No') and 2 < HEAD ≤ 4 and EDRTS ≤ 2.2 and (ARDS = 'Yes') and ISS ≤ 38 Then Dead | 12/15(80%) | C4.5 |
(MI = 'No') and (ID = 'Yes') and (Cg = 'Yes') and AGE > 61.6 Then Dead | 24/32(75%) | C4.5 |
(ID = 'No') and HEAD ≤ 3 and AGE ≤ 82.6 and ISS ≤ 22 Then Alive | 236/305(77.4%) | C4.5 |
HEAD ≤ 4 and (MI = 'No') and AGE ≤ 60.8 and ISS ≤ 38 Then Alive | 504/607(83%) | C4.5 |
HEAD ≤ 4 and AGE ≤ 78 and EDRTS > 7.55 and ISS ≤ 30 Then Alive | 207/263(78.7%) | C4.5 |
(MI = 'Yes') and ISS > 27 Then Dead | 50/60(83.3%) | C4.5 |
HEAD ≤ 3 and AGE ≤ 78 and 11 < ISS ≤ 27 Then Alive | 290/368(78.8%) | C4.5 |
(Cg = 'No') and HEAD ≤ 3 and AGE ≤ 78 Then Alive | 353/459(76.9%) | C4.5 |
(MI = 'Yes') and EDRTS ≤ 5.39 Then Dead | 41/51(80.4%) | C4.5 |
HEAD > 0 and (MI = 'Yes') and THORAX > 2 and (ID = 'No') Then Dead | 50/63(79.4%) | C4.5 |
(Cg = 'No') and (MI = 'No') and 2 < HEAD ≤ 4 and EDRTS ≤ 1.47 and (ARDS = 'Yes') and ISS ≤ 41 Then Dead | 13/17(76.5%) | C4.5 |
HEAD ≤ 4 and (MI = 'No') and AGE ≤ 62 and ISS ≤ 41 Then Alive | 555/678(81.9%) | C4.5 |
HEAD ≤ 4 and AGE ≤ 79.6 and EDRTS > 7.55 and ISS ≤ 30 Then Alive | 214/275(77.8%) | C4.5 |
(MI = 'No') and HEAD ≤ 4 and AGE ≤ 61.6 and ISS ≤ 34 Then Alive | 469/562(83.5%) | C4.5 |
HEAD ≤ 3 and AGE ≤ 61.6 and ISS ≤ 38 Then Alive | 420/503(83.5%) | C4.5 |
(MI = 'Yes') and (ID = 'Yes') and AGE > 68.5 Then Dead | 47/60(78.3%) | C4.5 |
(ARDS = 'No') and HEAD ≤ 3 and AGE ≤ 61.9 Then Alive | 276/326(84.7%) | C4.5 |
(ID = 'No') and (MI = 'Yes') and EDRTS > 5.39 and ISS ≤ 14 Then Alive | 21/25(84%) | C4.5 |
Extracted reliable rules for outcome prediction (> 85% accuracy)
Rules | Test Acc. | Method |
---|---|---|
HEAD ≤ 3 and AGE < 43.45 and FSBP < 143.50 and ISS ≤ 33 and EDRTS < 0.87 and THORAX ≥ 2 Then Rehab | 17/19(89.5%) | CART |
EDRTS < 5.36 and HEAD ≤ 3 and 33 ≤ FSBP ≤ 143 and ISS ≥ 33.50 Then Rehab | 69/79(87.3%) | CART |
HEAD ≥ 4 and FSBP < 171 and EDRTS < 2.25 Then Rehab | 125/135(92.6%) | CART |
2.25 ≤ EDRTS < 5.36 and HEAD ≥ 4 and FSBP < 171 and AGE ≥ 10.90 Then Rehab | 45/52(86.5%) | CART |
EDRTS ≥ 5.36 and AGE < 48.15 and THORAX ≥ 1 and ISS ≤ 21 Then Home | 23/27(85.2%) | CART |
EDRTS ≥ 5.36 and AGE ≥ 48.15 and EDGCSTOTAL ≥ 9 and ISS ≤ 25 Then Rehab | 61/65(93.8%) | CART |
EDRTS < 5.02 and HEAD ≤ 3 and 11.65 ≤ AGE < 24.40 and EDGCSTOTAL ≤ 8 and FSBP ≥ 108 and THORAX ≤ 4 Then Rehab | 24/28(85.7%) | CART |
EDRTS < 5.02 and HEAD ≤ 3 and 26.05 ≤ AGE < 37.30 and EDGCSTOTAL ≤ 8 and FSBP ≥ 108 Then Rehab | 22/24(91.7%) | CART |
EDRTS < 5.02 and HEAD ≤ 3 and AGE ≥ 43.30 Then Rehab | 50/55(90.9%) | CART |
EDRTS < 5.02 and HEAD ≥ 4 Then Rehab | 179/201(89.1%) | CART |
EDRTS ≥ 5.02 and AGE < 48.15 and THORAX ≥ 1 and ISS ≤ 21 Then Home | 23/27(85.2%) | CART |
EDRTS ≥ 5.02 and 24.25 ≤ AGE < 48.15 and THORAX ≥ 1 and 22 ≥ ISS < 28 and FSBP < 146 Then Home | 11/12(91.7%) | CART |
EDRTS ≥ 5.02 and AGE < 48.15 and THORAX ≥ 1 and ISS ≥ 22 and 74 ≤ FSBP ≤ 93 Then Rehab | 10/11(90.9%) | CART |
EDRTS ≥ 5.02 and AGE ≥ 48.15 and EDGCSTOTAL ≥ 9 and ISS ≥ 24.50 Then Rehab | 104/122(85.2%) | CART |
EDRTS < 2.69 and 113 ≤ FSBP ≤ 170 and HEAD ≤ 3 Then Rehab | 49/57(86.0%) | CART |
EDRTS < 2.69 and FSBP ≤ 170 and HEAD ≥ 4 Then Rehab | 126/137(92.0%) | CART |
EDRTS ≥ 2.69 and AGE < 45.10 and THORAX ≥ 1 and ISS ≤ 21 and EDGCSTOTAL ≥ 7 Then Home | 24/28(85.7%) | CART |
2.69 ≤ EDRTS < 5.02 and 22.80 ≤ AGE < 48.15 and THORAX ≥ 1 and ISS ≥ 22 and (ARDS = 'No') Then Rehab | 25/28(89.3%) | CART |
EDRTS ≥ 2.69 and 48.15 ≤ AGE < 84.30 and ISS ≥ 25 Then Rehab | 74/79(93.7%) | CART |
EDRTS < 5.02 and 39.25 ≤ AGE < 51.35 and HEAD ≤ 3 and FSBP ≤ 116 and ISS ≤ 33 Then Rehab | 12/13(92.3%) | CART |
EDRTS < 2.69 and AGE < 51.10 and HEAD ≥ 4 and FSBP < 179 Then Rehab | 117/126(92.9%) | CART |
2.69 ≤ EDRTS < 5.02 and 10.90 ≤ AGE < 51.10 and HEAD ≥ 4 and 82 ≤ FSBP < 179 and (ARDS = 'No') Then Rehab | 28/31(90.3%) | CART |
EDRTS < 5.02 and AGE ≥ 51.35 Then Rehab | 48/52(92.3%) | CART |
EDRTS ≥ 5.02 and AGE < 27.35 and THORAX < 1 and FSBP ≥ 123 and HEAD ≤ 4 Then Rehab | 13/15(86.7%) | CART |
EDRTS ≥ 5.02 and AGE < 48.15 and THORAX ≥ 1 and ISS ≤ 21 Then Home | 23/27(85.2%) | CART |
EDRTS ≥ 5.02 and AGE ≥ 48.15 and EDGCSTOTAL ≥ 7 and ISS ≥ 25 Then Rehab | 61/66(92.4%) | CART |
EDRTS < 5.02 and HEAD ≤ 2 and AGE ≥ 31.90 and ISS ≤ 39 and THORAX ≥ 2 Then Rehab | 23/26(88.5%) | CART |
EDRTS < 5.02 and HEAD ≥ 3 and AGE ≥ 23.15 Then Rehab | 249/281(88.6%) | CART |
EDRTS ≥ 5.02 and AGE ≥ 56.55 and EDGCSTOTAL ≥ 9 and ISS ≤ 24 and (ARDS = 'No') Then Rehab | 33/34(97.1%) | CART |
AGE < 24 and EDRTS < 0.58 and HEAD ≤ 3 and ISS ≤ 34 Then Rehab | 19/20(95.0%) | CART |
26.05 ≤ AGE < 47.15 and EDRTS < 4.75 and HEAD ≤ 3 and ISS ≤ 34 and THORAX ≥ 2 and EDGCSTOTAL ≤ 7 Then Rehab | 36/41(87.8%) | CART |
AGE < 48.15 and EDRTS < 4.75 and HEAD ≤ 3 and ISS ≥ 35 and EDGCSTOTAL ≤ 7 Then Rehab | 18/20(90.0%) | CART |
AGE < 48.15 and EDRTS < 2.69 and HEAD ≥ 4 Then Rehab | 115/126(91.3%) | CART |
23.15 ≤ AGE < 48.15 and EDRTS ≥ 4.75 and ISS ≥ 25 and HEAD ≤ 0 and FSBP ≥ 69 Then Rehab | 17/19(89.5%) | CART |
23.15 ≤ AGE < 26.75 and EDRTS ≥ 4.75 and ISS ≥ 25 and 1 ≤ HEAD ≤ 4 and FSBP ≥ 69 Then Rehab | 11/11(100.0%) | CART |
48.15 ≤ AGE < 85.70 and ISS ≥ 25 Then Rehab | 122/133(91.7%) | CART |
AGE < 48.15 and THORAX ≥ 1 and ISS ≥ 22 and EDRTS ≥ 5.36 and 73 ≤ FSBP < 120 Then Home | 20/22(90.9%) | CART |
EDRTS < 5.02 and HEAD ≤ 2 and AGE ≥ 30.25 Then Rehab | 31/35(88.6%) | C4.5 |
EDRTS < 5.02 and HEAD ≤ 3 and ISS ≥ 15 Then Rehab | 256/294(87.1%) | C4.5 |
EDRTS ≥ 5.02 and AGE ≤ 48.15 and EDGCSTOTAL ≥ 9 and ISS ≤ 24 and THORAX ≤ 3 and FSBP ≥ 93 Then Rehab | 110/127(86.6%) | C4.5 |
EDRTS ≥ 2.69 and AGE < 26.75 and ISS ≥ 25 and HEAD ≥ 5 Then Rehab | 33/37(89.2%) | C4.5 |
EDRTS ≥ 7.12 and 26.75 ≤ AGE < 43.25 and ISS ≥ 25 and 1 ≤ HEAD ≤ 2 Then Rehab | 15/17(88.2%) | C4.5 |
EDRTS < 2.69 and HEAD ≤ 3 and AGE < 38.30 and 108 ≤ FSBP < 192 Then Rehab | 38/43(88.4%) | C4.5 |
EDRTS < 2.69 and HEAD ≥ 4 Then Rehab | 132/146(90.4%) | C4.5 |
EDRTS ≥ 2.69 and AGE < 48.15 and 84 ≤ FSBP ≤ 93 Then Rehab | 18/21(85.7%) | C4.5 |
2.69 ≤ EDRTS < 4.75 and 11.65 ≤ AGE < 48.15 and FSBP ≥ 122 and (ARDS = 'No') Then Rehab | 33/36(91.7%) | C4.5 |
EDRTS ≥ 2.69 and AGE ≥ 48.15 and ISS ≥ 26 Then Rehab | 66/71(93.0%) | C4.5 |
EDGCSTOTAL ≤ 5 and ISS ≥ 15 and FSBP ≤ 177 and THORAX ≥ 4 Then Rehab | 252/284(88.7%) | C4.5 |
EDGCSTOTAL ≥ 6 and AGE ≥ 48.15 and ISS ≥ 26 Then Rehab | 66/72(91.7%) | C4.5 |
THORAX ≤ 2 and AGE ≤ 33.9 and EDRTS ≤ 5.03 Then Rehab | 62/72(86.1%) | C4.5 |
(ID = 'Yes') and (Cg = 'No') Then Rehab | 11/12 (91.7%) | C4.5 |
HEAD ≤ 0 and THORAX ≤ 1 and AGE ≤ 59.7 and ISS > 5 Then Rehab | 28/32(87.5%) | C4.5 |
Extracted supporting rules for outcome prediction (75% – 85% accuracy)
Rules | Test Acc. | Method |
---|---|---|
EDRTS ≥ 5.36 and EDGCSTOTAL ≥ 9 and ISS ≤ 24 and THORAX ≤ 3 and AGE ≥ 53.95 and FSBP ≥ 93 Then Rehab | 49/62(79.0%) | CART |
EDRTS ≥ 7.12 and AGE < 47.55 and THORAX ≥ 1 and 28 ≤ ISS < 35 and 94 ≤ FSBP ≤ 135 Then Rehab | 16/20(80.0%) | CART |
EDRTS ≥ 2.69 and AGE < 22.80 and THORAX ≥ 1 and ISS ≥ 22 and 123 ≤ FSBP ≤ 139 Then Rehab | 11/13(84.6%) | CART |
EDRTS ≥ 7.70 and 22.80 ≤ AGE < 45.90 and THORAX ≥ 1 and ISS ≥ 28 and FSBP ≥ 76 Then Rehab | 31/39(79.5%) | CART |
5.02 ≤ EDRTS < 7.12 and AGE < 45.90 and THORAX ≥ 1 and 22 ≤ ISS ≤ 39 Then Rehab | 9/12(75.0%) | CART |
EDRTS ≥ 7.12 and AGE < 48.15 and ISS ≥ 25 and HEAD ≤ 4 and THORAX ≥ 1 and 69 ≤ FSBP < 98 Then Rehab | 15/19(78.9%) | CART |
EDRTS ≥ 2.69 and AGE < 47.80 and ISS ≤ 24 and HEAD ≤ 2 and Then Home | 43/56(76.8%) | CART |
2.69 ≤ EDRTS < 5.02 and 26.75 ≤ AGE < 47.80 and ISS ≥ 25 and HEAD ≥ 1 Then Rehab | 28/34(82.4%) | CART |
EDRTS ≥ 2.69 and ISS ≤ 24 and THORAX ≤ 3 and HEAD ≥ 3 Then Rehab | 151/182(83%) | CART |
EDRTS ≥ 4.75 and AGE < 48.15 and FSBP ≥ 94 and THORAX ≥ 1 and ISS ≤ 21 Then Home | 21/25(84.0%) | CART |
EDRTS ≥ 2.69 and AGE ≥ 48.15 and ISS ≤ 25 and THORAX ≤ 3 and FSBP ≥ 80 Then Rehab | 59/74(79.7%) | CART |
EDGCSTOTAL ≥ 7 and 26.75 ≤ AGE < 43.00 and ISS ≥ 25 and FSBP ≥ 138 and HEAD ≥ 3 Then Rehab | 12/16(75.0%) | CART |
EDGCSTOTAL ≥ 6 and AGE ≥ 50.60 and ISS ≤ 25 and THORAX ≥ 3 and HEAD ≤ 4 and FSBP ≥ 74 Then Rehab | 61/79(77.2%) | CART |
(ID = 'Yes') and AGE > 44 and (ARDS = 'Yes') Then Rehab | 30/39(76.9%) | C4.5 |
THORAX ≤ 3 and ISS > 18 Then Rehab | 342/431(79.4%) | C4.5 |
(Cg = 'No') and 18.4 < AGE ≤ 59.7 and ISS > 30 Then Rehab | 162/199(81.4%) | C4.5 |
Extracted reliable rules for ICU days prediction (> 85% accuracy)
Rules | Test Acc. | Method |
---|---|---|
(AIRWAY = 'Need') and 115 ≤ ED-BP < 156 and AGE ≥ 47.05 and Then ICU stay days ≥ 3 | 14/15(93.3%) | CART |
(AIRWAY = 'Need') and 115 ≤ ED-BP < 156 and ED-RESP < 18 and 4.35 ≤ AGE < 14.5 Then ICU stay days ≥ 3 | 12/12(100%) | CART |
(AIRWAY = 'No Need') and ED-RESP ≥ 21 and 45 ≤ AGE < 55.85 Then ICU stay days ≤ 2 | 10/11(90.1%) | CART |
(AIRWAY = 'Need') and ED-BP < 91 Then ICU stay days ≥ 3 | 14/14(100%) | CART |
(AIRWAY = 'Need') and 93.5 ≤ ED-BP < 156.5 and ED-PULSE ≥ 60.5 and AGE ≥ 54.2 Then ICU stay days ≥ 3 | 10/10(100%) | CART |
(AIRWAY = 'Need') and 94 ≤ ED-BP < 156 and ED-PULSE ≥ 61 and ED- RESP < 19 and 18.45 ≤ AGE < 44.5 Then ICU stay days ≥ 3 | 60/76(86.6%) | CART |
(AIRWAY = 'No Need') and AGE < 52.9 and ED-BP ≥ 107 and ED- GCS ≥ 11 Then ICU stay days ≤ 2 | 175/192(91.1%) | CART |
(AIRWAY = 'Need') and ED-BP < 150.5 and ED-RESP < 19 and AGE ≥ 4.9 and ED-PULSE ≥ 138 Then ICU stay days ≥ 3 | 18/20(90%) | CART |
(AIRWAY = 'Need') and ED-RESP < 19 and ED-PULSE < 138 and ED- BP < 115 and 10.9 ≤ AGE < 47.3 Then ICU stay days ≥ 3 | 31/33 (93.9%) | CART |
(AIRWAY = 'No Need') and AGE < 37.1 and ED-GCS ≥ 11 and ED- BP ≥ 125 Then ICU stay days ≤ 2 | 89/90(98.9%) | CART |
(AIRWAY = 'No Need') and AGE < 37.1 and ED-GCS ≥ 11 and ED- BP < 119 Then ICU stay days ≤ 2 | 39/44(88.6%) | CART |
(AIRWAY = 'No Need') and AGE < 37.1 and ED-GCS ≥ 13 and 119 ≤ ED- BP < 125 and ED-PULSE ≥ 90 Then ICU stay days ≤ 2 | 21/22(95.5%) | CART |
(AIRWAY = 'Need') and 146 ≤ ED-BP < 156 and AGE < 22.5 Then ICU stay days ≥ 3 | 11/12(91.2%) | CART |
(AIRWAY = 'No Need') and AGE < 37.05 and ED-GCS ≥ 9 Then ICU stay days ≤ 2 | 157/172(91.3%) | CART |
(AIRWAY = 'No Need') and 37.05 ≤ AGE < 46.9 and ED-RESP < 21 and ED-PULSE < 121 Then ICU stay days ≤ 2 | 23/25(92%) | CART |
(AIRWAY = 'No Need') and ED-RESP < 21 and ED-PULSE < 121 and AGE ≥ 49.7 and ED-BP ≥ 141 Then ICU stay days ≤ 2 | 12/13(92.3%) | CART |
(AIRWAY = 'No Need') and AGE < 37.05 and ED-GCS < 10 and 114 ≤ ED- BP < 142 Then ICU stay days ≤ 2 | 11/12(91.7%) | CART |
(AIRWAY = 'Need') and ED-BP < 91.5 Then ICU stay days ≥ 3 | 14/14(100%) | CART |
(AIRWAY = 'Need') and 91 ≤ ED-BP < 156 and 95.5 ≤ ED-PULSE < 102.5 Then ICU stay days ≥ 3 | 15/17(88.2%) | CART |
(AIRWAY = 'No Need') and AGE < 52.9 and ED-BP ≥ 99 and ED- GCS ≥ 13 Then ICU stay days ≤ 2 | 177/196(90.3%) | CART |
(AIRWAY = 'No Need') and ED-BP < 134 and 37.05 ≤ AGE < 67.35 and ED-RESP < 19 Then ICU stay days ≤ 2 | 11/12(91.7%) | CART |
(AIRWAY = 'Need') and ED-PULSE ≥ 62 and AGE ≥ 24.35 and ED- BP < 110 Then ICU stay days ≥ 3 | 26/29(89.7%) | CART |
(AIRWAY = 'Need') and 110 ≤ ED-BP < 180 and ED-PULSE ≥ 62 and 47.05 ≤ AGE < 68.2 Then ICU stay days ≥ 3 | 16/17(94.1%) | CART |
(AIRWAY = 'No Need') and AGE < 37 and ED-GCS ≥ 13 Then ICU stay days ≤ 2 | 147/159(92.5%) | CART |
(AIRWAY = 'No Need') and AGE ≥ 37 and 135 ≤ ED-BP < 163 Then ICU stay days ≤ 2 | 26/29(89.7%) | CART |
(AIRWAY = 'Need') and ED-PULSE ≥ 62 and AGE ≥ 24.35 and ED- BP < 110 Then ICU stay days ≥ 3 | 31/35(88.6%) | CART |
(AIRWAY = 'Need') and ED-BP < 91 Then ICU stay days ≥ 3 | 14/14(100%) | CART |
(AIRWAY = 'Need') and 93 ≤ ED-BP < 156 and ED-RESP < 19 and AGE ≤ 54.05 Then ICU stay days ≥ 3 | 10/10(100%) | CART |
(AIRWAY = 'Need') and ED-RESP < 19 and 93 ≤ ED-BP < 119 and 18.45 ≤ AGE < 47.3 Then ICU stay days ≤ 3 | 24/26(92.3%) | CART |
(AIRWAY = 'No Need') and ED-GCS ≥ 11 and ED-BP ≥ 88 and AGE < 37.05 Then ICU stay days ≤ 2 | 148/160(92.5%) | CART |
Age ≤ 42 and (Airway = 'No Need') and ED-PULSE ≤ 137 and ED- RESP > 19 Then ICU stay days ≤ 2 | 100/116(86.2%) | C4.5 |
Age > 37 and ED-BP ≤ 95 Then ICU stay days ≥ 3 | 14/14(100%) | C4.5 |
Extracted supporting rules for ICU days prediction (75% – 85% accuracy)
Rules | Test Acc. | Method |
---|---|---|
(AIRWAY = 'No Need') and ED-RESP < 21 and ED-BP < 142 ED- PULSE < 79 and 37.05 ≤ AGE < 44.15 Then ICU stay days ≤ 2 | 13/16(81.3%) | CART |
(AIRWAY = 'No Need') and AGE ≥ 52.9 and ED-BP ≥ 141 Then ICU stay days ≤ 2 | 12/15(80%) | CART |
(AIRWAY = 'Need') and 117 ≤ ED-BP < 135 and ED-RESP < 19 and 68 ≤ ED-PULSE < 138 and 15.05 ≤ AGE < 46.4 Then ICU stay days ≥ 3 | 23/28(82.1%) | CART |
(AIRWAY = 'Need') and 136 ≤ ED-BP < 150 and ED-RESP < 19 and ED-PULSE < 138 and 15.05 ≤ AGE < 23.25 Then ICU stay days ≥ 3 | 10/13(77%) | CART |
(AIRWAY = 'No Need') and 96 ≤ ED-BP < 163 and 39.15 ≤ AGE < 69.05 Then ICU stay days ≤ 2 | 44/55(80%) | CART |
(AIRWAY = 'Need') and ED-BP < 156 and AGE ≥ 24.35 Then ICU stay days ≥ 3 | 76/96(79.2%) | CART |
(AIRWAY = 'Need') and ED-BP < 146 and AGE < 17.85 and 135 ≤ ED-PULSE < 181 Then ICU stay days ≥ 3 | 9/11(81.2%) | CART |
(AIRWAY = 'Need') and ED-BP < 146.5 and AGE < 17.85 and ED- PULSE < 131 and ED-RESP < 18 Then ICU stay days ≥ 3 | 15/20(75%) | CART |
(AIRWAY = 'No Need') and ED-BP ≥ 141 Then ICU stay days ≤ 2 | 223/265(84.2%) | CART |
(AIRWAY = 'Need') and ED-BP < 114 Then ICU stay days ≥ 3 | 44/52(84.6%) | CART |
(AIRWAY = 'Need') and 114 ≤ ED-BP < 135.5 and ED-PULSE < 97 and 17.2 ≤ AGE < 46.95 and ED-RESP < 7 Then ICU stay days ≥ 3 | 10/13(77%) | CART |
(AIRWAY = 'No Need') and AGE ≥ 52.9 and ED-BP ≥ 141 Then ICU stay days ≤ 2 | 12/15(80%) | CART |
(AIRWAY = 'Need') and ED-BP < 114 Then ICU stay days ≥ 3 | 44/52(84.6%) | CART |
(AIRWAY = 'Need') and 110.5 ≤ ED-BP < 180.5 and ED- PULSE ≥ 62 and 4.35 ≤ AGE < 44.5 and ED-GCS < 10 Then ICU stay days ≥ 3 | 35/46(76.1%) | CART |
(AIRWAY = 'No Need') and AGE ≥ 37 and 135 ≤ ED-BP < 163 Then ICU stay days ≤ 2 | 31/35(88.6%) | CART |
(AIRWAY = 'No Need') and 37.05 ≤ AGE < 55.6 and ED-GCS ≥ 11 and 88 ≤ ED-BP < 163 Then ICU stay days ≤ 2 | 40/49(81.6%) | CART |
ED-BP > 100 and ED-RESP > 19 Then ICU stay days ≤ 2 | 145/180(80.6%) | C4.5 |
Discussion
We developed a computer-aided rule-base using significant variables selected via logistic regression, and showed that the approximations of the variables help increase rule quality. Our intent is to extract and formulate medical diagnostic knowledge into an appropriate set of transparent decision rules that can be used in a computer-assisted decision making system. The proposed method extracts the most significant variables using logistic regression with direct maximization likelihood estimation. By comparing the performances using five machine learning algorithms – AdaBoost, C4.5, CART, RBF neural network, and SVM – using all available variables and significant variables only, we found that using only the most significant variables provides a considerable improvement in performance. All five methods show improvement across all-available and significant-variables-only, indicating that our proposed selection method is robust and efficient.
Rule sensitivity and specificity
Off-site Dataset | Off-site Dataset | Helicopter Dataset | |
---|---|---|---|
Predictive Outcome | Alive/Dead | Home/Rehab | ICU stay Days |
Sensitivity (> 85% rules) | 91.9% | 88.7% | 90.6% |
Specificity (> 85% rules) | 89.2% | 87.7% | 91% |
Sensitivity (75%–85% rules) | 86.2% | 79% | 82.5% |
Specificity (75%–85% rules) | 80.4% | 80.1% | 80.4% |
One important issue in rule selection is how to deal with rules with accuracy below 85%. When using only the over-85% rules, some medical knowledge in the database might have been ignored. The accuracy of a rule may be low due to the lack of "database completeness", rather than a flaw in the rule itself. Therefore, rules with less than 85% accuracy cannot be completely removed from the rule based system. We will instead use those rules as additional "supporting rules" in suggesting possible treatment. For example, according to trauma experts, patients with a high ISS score (> 25) are least likely to survive. However, we found some rules with surprising implications. For instance, one of these "counterintuitive" rules pointed to the fact that there are 52 alive cases (3.3%) with ISS high scores (38). Of these 52 patients, 33 (63.5%) have high AIS head scores (≥ 4), and 38 patients (73%) are male. Considering the above conditions, surviving patients have lower thorax (average score = 2.61) and lower abdomen AIS scores (average score = 1.03) than fatal cases. These fatal cases typically have a higher head AIS score (average score = 5.08) than surviving patients (average head score = 3.90). In addition, we found that none of the surviving patients have complications such as coagulopathy, and only a few had a pre-existing disease (in particular, Insulin Dependency and Myocardial Infarction).
While only Acute Respiratory Distress Syndrome (ARDS) is usually considered an impact factor in predicted survival, according to the created rules, pre-existing conditions, Acute Respiratory Distress Syndrome (ARDS), Insulin Dependency, Myocardial Infarction, and Coagulopathy all have significant impact. Also, airway status (need/not need) was identified as a primary factor in predicting the number of ICU days for patients transported via helicopter.
Note that for ICU length of stay prediction, 74.6% of patients stayed at in ICU less than 2 days. Only 25.4% of patients stayed more than 2 days, and only 2.9% of those were in ICU for more than 20 days. This reinforces Eckhart's point that many patients are transported via helicopter unnecessarily. Therefore, the use of accurate ICU days prediction rules may help improve the efficiency of helicopter transport, considering cost effectiveness as well as the treatment of patients in critical condition.
Conclusion
The results in this paper provide a framework to improve the physicians' diagnostic accuracy with the aid of machine learning algorithm. The resulting system is effective in predicting patient survival, and rehab/home outcome. A method has been introduced that creates a variety of reliable rules that make sense to physicians by combining CART and C4.5 and using only significant variables extracted via logistic regression. The resulting computer-aided decision-making system has significant benefits, both in providing rule-based recommendations and in enabling optimal resource utilization. This may ultimately assist physicians in providing the best possible care to their patients. The diagnosis of future patients may also be improved by analyzing all possible rules associated with their symptoms.
The system will be tested at all 17 hospitals of the Carolinas Healthcare System (CHS). Software that provides the computer-aided decision making system will be optimized and made available to the academic community as a web-based application, as well as a software tool on portable personal computing devices. Feedback from every hospital will then be considered and used to validate and improve the system.
Declarations
Acknowledgements
This research was partially funded by research grants from Health Services Foundation, Carolinas HealthCare System, and Virginia Commonwealth University. This material is based upon work supported by the National Science Foundation under Grant No.IIS0758410. The authors would like to thank these institutions for their support.
Authors’ Affiliations
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