Improvement and automation of artificial neural networks to estimate medical outcomes

Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit

In the neonatal intensive care unit, the early and accurate prediction of mortality, length of stay and duration of ventilation can improve decision making. For physiological events, non-linear prediction models generally out-perform statistical-based approaches, as was confirmed in these experiments. For three medical outcomes, the maximum-likelihood (ML) approximation was used in conjunction with a gradient descent artificial neural network (ANN) prototype to create models with risk estimation ranges. The ML ANN showed that the ML estimation function was successful at creating variable sensitivity models for three important outcomes. The flexibility of the ML ANN in terms of output values differentiates it from the more traditional ANN.

A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease

The primary objective of this study was to classify patients with CAD as regards their gluco-metabolic state by easily available clinical variables. A secondary objective was to explore if it was possible to identify CAD patients at a high cardiovascular risk due to metabolic perturbations. The 1,867 patients with CAD were gluco-metabolically classified by an OGTT. Among these, 990 patients had complete data regarding all components of the metabolic syndrome, BMI, HbA1c and medical history. Only FPG and HDL-c adjusting for age significantly impacted OGTT classification. Based on these variables, a neural network reached a cross-validated misclassification rate of 37.8% compared with OGTT. By this criterion, 1,283 patients with complete one-year follow-up concerning all-cause mortality, myocardial infarction and stroke (CVE) were divided into low- and high-risk groups within which CVE were, respectively, 5.1 and 9.4% (p=0.016).Adjusting for confounding variables the relative risk for a CVE based on the neural network was 2.06 (95% CI: 1.18-3.58) compared with 1.37 (95% CI: 0.79-2.36) for OGTT.

CONCLUSIONS

The neural network, based on FPG, HDL-c and age, showed useful risk stratification capacities; it may, therefore, be of help when stratifying further risk of CVE in CAD patients.

Imputation of missing values by integrating neural networks and case-based reasoning

Missing values in a medical database present a problem when trying to develop a prediction model for a broad range of patients, if the data are not missing at random. We present a data imputation approach for physiologic parameters that incorporates individualized case information into the imputed values. We replaced missing values in a neonatal intensive care unit (NICU) database with relevant data by integrating aspects of artificial neural networks (ANNs) and case-based reasoning (CBR).

Logistic model tree extraction from artificial neural networks

Artificial neural networks (ANNs) are a powerful and widely used pattern recognition technique. However, they remain "black boxes" giving no explanation for the decisions they make. This paper presents a new algorithm for extracting a logistic model tree (LMT) from a neural network, which gives a symbolic representation of the knowledge hidden within the ANN. Landwehr's LMTs are based on standard decision trees, but the terminal nodes are replaced with logistic regression functions. This paper reports the results of an empirical evaluation that compares the new decision tree extraction algorithm with Quinlan's C4.5 and ExTree. The evaluation used 12 standard benchmark datasets from the University of California, Irvine machine-learning repository. The results of this evaluation demonstrate that the new algorithm produces decision trees that have higher accuracy and higher fidelity than decision trees created by both C4.5 and ExTree.

The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors

BACKGROUND

The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritize medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients.

METHODS

A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset.

RESULTS

The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90.

CONCLUSIONS

This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.

Predicting high-risk preterm birth using artificial neural networks

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

Risk factors for Apgar score using artificial neural networks

Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.

Artificial neural networks for diagnosis and survival prediction in colon cancer

ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.