Contributions from computational intelligence to healthcare data processing

80 p.The increasing ability to gather, store and process health care information, through the electronic health records and improved communication methods opens the door for new applications intended to improve health care in many different ways. Crucial to this evolution is the development of new computational intelligence tools, related to machine learning and statistics. In this thesis we have dealt with two case studies involving health data. The first is the monitoring of children with respiratory diseases in the pediatric intensive care unit of a hospital. The alarm detection is stated as a classification problem predicting the triage selected by the nurse or medical doctor. The second is the prediction of readmissions leading to hospitalization in an emergency department of a hospital. Both problems have great impact in economic and personal well being. We have tackled them with a rigorous methodological approach, obtaining results that may lead to a real life implementation. We have taken special care in the treatment of the data imbalance. Finally we make propositions to bring these techniques to the clinical environment

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Comparing predictions made by a prediction model, clinical score, and physicians Pediatric asthma exacerbations in the emergency department

Background: Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. Objectives: First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. Design: A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2, data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Measurements: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results: In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant. Conclusion: Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy

Reliability and validity of triage systems in paediatric emergency care

Background. Triage in paediatric emergency care is an important tool to prioritize seriously ill children. Triage can also be used to identify patients who do not need urgent care and who can safely wait. The aim of this review was to provide an overview of the literature on reliability and validity of current triage systems in paediatric emergency care. Methods. We performed a search in Pubmed and Cochrane on studies on reliability and validity of triage systems in children. Results. The Manchester Triage System (MTS), the Emergency Severity Index (ESI), the Paediatric Canadian Triage and Acuity Score (paedCTAS) and the Australasian Triage Scale (ATS) are common used triage systems and contain specific parts for children. The reliability of the MTS is good and reliability of the ESI is moderate to good. Reliability of the paedCTAS is moderate and is poor to moderate for the ATS. The internal validity is moderate for the MTS and confirmed for the CTAS, but not studied for the most recent version of the ESI, which contains specific fever criteria for children. Conclusion. The MTS and paedCTAS both seem valid to triage children in paediatric emergency care. Reliability of the MTS is good, moderate to good for the ESI and moderate for the paedCTAS. More studies are necessary to evaluate if one triage system is superior over other systems when applied in emergency care

The danish regions pediatric triage model has a limited ability to detect both critically ill children as well as children to be sent home without treatment:a study of diagnostic accuracy

Abstract Background The Danish Regions Pediatric Triage model (DRPT) was introduced in 2012 and subsequent implemented in most Danish acute pediatric departments. The aim was to evaluate the validity of DRPT as a screening tool to detect both the most serious acute conditions and the non-serious conditions in the acute referred patients in a pediatric department. Method The study was prospective observational, with follow-up on all children with acute referral to pediatric department from October to December 2015. The DRPT was evaluated by comparison to a predefined reference standard and to the actual clinical outcomes: critically ill children and children returned to home without any treatment. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy and likelihood for positive and negative test were calculated. Results Five hundred fifty children were included. The DRPT categorized 7% very urgent, 28% urgent, 29% standard and 36% non-urgent. The DRPT was equal to the reference standard in 31% of the children (CI: 27-35%). DRPT undertriaged 55% of the children (CI: 51-59%) and overtriaged 14% of the children (CI: 11-17%). For the most urgent patients the sensitivity of DRPT was 31% (CI: 20-48%) compared to the reference standard and 20% (CI: 7-41) for critically ill. For children with non-urgent conditions the specificity of DRPT was 66% (CI: 62-71%) compared to the reference standard and 68% (CI: 62-75%) for the children who went home with no treatment. In none of the analyses, the likelihood ratio of the negative test was less than 0.7 and the positive likelihood ratio only reached more than 5 in one of the analyses. Discussion This study is the first to evaluate the DRPT triage system. From the very limited validity studies of other well-established triage systems, it is difficult to judge whether the DRPT performs better or worse than the alternatives. The DRPT errs to the undertriage side. If the sensitivity is low, a number of the sickest children are undetected and this is a matter of concern. Conclusion The DRPT is a triage tool with limited ability to detect the critically ill children as well as the children who can be returned to home without any treatment. Trial registration Not relevan

Performance characteristics of five triage tools for major incidents involving traumatic injuries to children

Context Triage tools are an essential component of the emergency response to a major incident. Although fortunately rare, mass casualty incidents involving children are possible which mandate reliable triage tools to determine the priority of treatment. Objective To determine the performance characteristics of five major incident triage tools amongst paediatric casualties who have sustained traumatic injuries. Design, setting, participants Retrospective observational cohort study using data from 31,292 patients aged less than 16 years who sustained a traumatic injury. Data were obtained from the UK Trauma Audit and Research Network (TARN) database. Interventions Statistical evaluation of five triage tools (JumpSTART, START, CareFlight, Paediatric Triage Tape/Sieve and Triage Sort) to predict death or severe traumatic injury (injury severity score >15). Main outcome measures Performance characteristics of triage tools (sensitivity, specificity and level of agreement between triage tools) to identify patients at high risk of death or severe injury. Results Of the 31,292 cases, 1029 died (3.3%), 6842 (21.9%) had major trauma (defined by an injury severity score >15) and 14,711 (47%) were aged 8 years or younger. There was variation in the performance accuracy of the tools to predict major trauma or death (sensitivities ranging between 36.4 and 96.2%; specificities 66.0–89.8%). Performance characteristics varied with the age of the child. CareFlight had the best overall performance at predicting death, with the following sensitivity and specificity (95% CI) respectively: 95.3% (93.8–96.8) and 80.4% (80.0–80.9). JumpSTART was superior for the triaging of children under 8 years; sensitivity and specificity (95% CI) respectively: 86.3% (83.1–89.5) and 84.8% (84.2–85.5). The triage tools were generally better at identifying patients who would die than those with non-fatal severe injury. Conclusion This statistical evaluation has demonstrated variability in the accuracy of triage tools at predicting outcomes for children who sustain traumatic injuries. No single tool performed consistently well across all evaluated scenarios