A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

BACKGROUND: The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules. METHODS: We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing. RESULTS: Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data. CONCLUSIONS: The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis

Supporting Acute Appendicitis Diagnosis: A Pre-Clustering-Based Classification Technique

Service quality and cost containment represent two critical challenges in healthcare management. Toward that end, acute appendicitis, a common surgical condition, is important and requires timely, accurate diagnosis. The diverse and atypical symptoms make such diagnoses difficult, thus resulting in increased morbidity and negative appendectomy. While prior research has recognized the use of classification analysis to support acute appendicitis diagnosis, the skewed distribution of the cases pertaining to positive or negative acute appendicitis has significantly constrained the effectiveness of the existing classification techniques. In this study, we develop a pre-clustering-based classification (PCC) technique to address the skewed distribution problem common to acute appendicitis diagnosis. We empirically evaluate the proposed PCC technique with 574 clinical cases of positive and negative acute appendicitis obtained from a tertiary medical center in Taiwan. Our evaluation includes tradition support vector machine, a prevalent resampling classification technique, Alvarado scoring system, and a multi-classifier committee for performance benchmark purposes. Our results show the PCC technique more effective and less biased than the benchmark techniques, without favoring the positive or negative class

From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique

For reasoning under uncertainty the Bayesian network has become the representation of choice. However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism for inference in Singly Connected Networks. We call this a Mutual Information Measure classifier, as it corresponds to the restricted class of trees built from mutual information. We show that the new approach provides for both an efficient and localised method of classification, with performance accuracies comparable with the less restricted general Bayesian networks. To improve the performance of the classifier, we additionally investigate the possibility of expanding the class Markov blanket by use of a Wrapper approach and further show that the performance can be improved by focusing on the class Markov blanket and that the improvement is not at the expense of increased complexity. Finally, the two methods are applied to the task of diagnosing the 'real' world medical domain, Acute Abdominal Pain. Known to be both a different and challenging domain to classify, the objective was to investigate the optiniality claims, in respect of the Naive Bayes classifier, that some researchers have argued, for classifying in this domain. Despite some loss of representation capabilities we show that the Mutual Information Measure classifier can be effectively applied to the domain and also provides a recognisable qualitative structure without violating 'real' world assertions. In respect of its 'selective' variant we further show that the improvement achieves a comparable predictive accuracy to the Naive Bayes classifier and that the Naive Bayes classifier's 'overall' performance is largely due the contribution of the majority group Non-Specific Abdominal Pain, a group of exclusion

Methodological overview of systematic reviews to establish the evidence base for emergency general surgery

BACKGROUND: The evidence for treatment decision‐making in emergency general surgery has not been summarized previously. The aim of this overview was to review the quantity and quality of systematic review evidence for the most common emergency surgical conditions. METHODS: Systematic reviews of the most common conditions requiring unplanned admission and treatment managed by general surgeons were eligible for inclusion. The Centre for Reviews and Dissemination databases were searched to April 2014. The number and type (randomized or non‐randomized) of included studies and patients were extracted and summarized. The total number of unique studies was recorded for each condition. The nature of the interventions (surgical, non‐surgical invasive or non‐invasive) was documented. The quality of reviews was assessed using the AMSTAR checklist. RESULTS: The 106 included reviews focused mainly on bowel conditions (42), appendicitis (40) and gallstone disease (17). Fifty‐one (48·1 per cent) included RCTs alone, 79 (74·5 per cent) included at least one RCT and 25 (23·6 per cent) summarized non‐randomized evidence alone. Reviews included 727 unique studies, of which 30·3 per cent were RCTs. Sixty‐five reviews compared different types of surgical intervention and 27 summarized trials of surgical versus non‐surgical interventions. Fifty‐seven reviews (53·8 per cent) were rated as low risk of bias. CONCLUSION: This overview of reviews highlights the need for more and better research in this field

The evaluation and enhancement of case driven diagnostic advice systems: a study in three domains

Relevant literature has been reviewed regarding the performance, implementation and evaluation of computer based medical decision support systems. The diagnostic performance of five simple case driven acute chest pain advice systems, have been compared using a standardized set of clinical records. A Bayesian inference model demonstrated superiority over two derived by logistic regression. Small data set flow charts performed well but both relied upon the use of expert opinion. A Bayesian acute abdominal pain diagnostic advice system has been evaluated in a clinical trial. Standardized data collection improved the diagnostic performance of doctors. In practice, the computer system offered little additional user benefit. From further tests in primary care, it was concluded that, whereas general practitioners might enhance their performance by using data collection sheets, paramedics might benefit through direct use of the computer. DERMIS is a new dermatology primary care diagnostic advice system. Components include a database derived from 5203 prospectively collected clinical records, a user interface, and an enhanced Bayesian inference model incorporating combined frequency estimates, expert beliefs and rationalized end-point groups. On laboratory testing, the diagnostic accuracy of DERMIS was 83%. The correct diagnosis appeared in the top three, of a possible 42 disease list on 97% of occasions. In a semi-field trial of DERMIS involving 49 general practitioners, doctors did not always collect the same information as a dermatologist but were able to significantly increase their chance of making a correct diagnosis through use of the computer system. It has been concluded that although implementation of DERMIS might well increase general practitioner diagnostic accuracy and lead to improvements in the management of skin disease in primary care, rates of referral for specialist opinion might not be affected unless standard management plans are adopted

Identifying quality in the delivery of emergency general surgery

The delivery of high-quality emergency general surgical care remains a concern for clinicians, healthcare providers and policy makers. Emergency admissions contribute to approximately half of a general surgeon’s workload, however the morbidity and mortality figures seen in this cohort are up-to ten times higher than those seen in elective practice. Despite considerable advances in surgical technology and peri/post-operative protocols over the past twenty years, there appears to be little improvement in outcome following emergency surgical admissions. It is therefore proposed that the delivery of emergency surgical services and hospital structure may significantly contribute to the poor outcomes seen in the acute setting and a greater understanding of the factors that contribute to high-quality care is required. An introduction to the factors that contribute to the delivery of emergency general surgery is presented along with the concepts of examining and identifying quality both in healthcare and other high-risk industries. A systematic review then examines the different models of care seen in the delivery of emergency general surgery across the world along with their effect on outcome and sets the scene for the areas of interest in this thesis. A series of inter-linked, mixed methods studies combining: quantitative analyses of an international dataset, ethnographic observation, a healthcare failure mode effects analysis and audit to identify structural factors that lead to improved outcomes in the delivery of emergency general surgery. The themes of high-quality care, hospital structure, international benchmarking and their association with outcome run throughout these studies in this thesis with outcome data from hospitals in Australia, the United Kingdom and the United States being compared. This thesis highlights a series of unit-level quality indicators whose introduction can be associated with high-quality care and be directly translated into clinical practice using quality improvement methodologies to ultimately improve patient care.Open Acces

A Rule Based Classification Model to Predict Colon Cancer Survival

Introduction: Colon cancer is the second most common cancer in the world and fourth most common cancer in both sexes in Iran, whose % 8.12 of all cancers in the covers. Predict the outcome of cancer and basic clinical data about it is very important. Data mining techniques can be used to predict cancer outcome. In our country, data mining studies on colon cancer, not covered as lung or breast cancers. It seems can be with identify factors influencing on survival and modify them, increased survival of colon cancer patients. Then according to high rates of colon cancer and the benefits of data mining to predict survival, in this study examined factors influencing on the survival of these patients. Materials and Methods: We use a dataset with four attributes that include the records of 570 patients in which 327 Patients (57.4%) and 243 (42.6%) patients were males and females respectively. Trees Random Forest (TRF), AdaBoost (AD), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of colon cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Results: Out of 570 patients, 338 patients and 232 patients were alive and dead respectively. In this Study, at first sight it seems that among this techniques, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (AD, RBFN and MLP). The accuracy, sensitivity, specificity and the area under ROC curve of TRF are 0.76, 0.808, 0.70 and 0.83, respectively. Conclusions: In this study seems that Trees Random Forest model (TRF) which is a rule based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for colon cancer survival prediction as well as medical decision making

The Role of Visual Abstracts in the Dissemination of Medical Research

Medical research within the UK has continued to grow, most notably during the COVID-19 pandemic over the last two years, which highlights the importance of disseminating relevant research findings. For all researchers involved in clinical trials and scientific research, the end goal of success is not completed following the publication of the research findings, but ultimately true impact and significance is achieved when such research has a role in developing clinical practice. Each year between 2.5 - 3 million scientific papers are published and the number continues to rise, therefore it is becoming increasingly difficult to ensure that published research has such a targeted impact as it must first get noticed. Increasing time commitments result in difficulties for clinicians keeping up-to-date with the current literature and in order to address this, journals and researchers have developed approaches to share peer-reviewed research with the wider research community in an effective and efficient manner. One such approach has been the introduction of the visual abstract which comprises of an infographic style format, coupled with a shortened, limited word summary of the research abstract detailing the key question, methodology, findings and take home message of the research study. The visual abstract has characteristics which enable it to be shared on social media platforms and in turn increase the interest and impact within the research community. Visual abstracts are being increasingly introduced within medical journals and organisations to help disseminate valuable research findings. This review focuses on visual abstracts, what they are, their history, structure and role within research dissemination and medical education

Low value care in surgery

Background Value has been defined as the ratio of quality outcomes to cost. Perfect value would represent infinitely beneficial outcomes associated with minimal costs. Of interest to the present study are interventions where outcomes are minimal, and costs may be high as they may provide an opportunity for disinvestment, improving the overall value of care whilst providing efficiency gains. Methods A Scoping Narrative Review was performed in order to understand incumbent approaches towards dealing with low value care. International lessons from different processes were identified and encompassed into a conceptual logic orientated framework for de-adoption. To identify low value care in surgery a Systematic review of peer reviewed high-level literature was performed to identify candidate interventions for de-adoption. Subsequently a granular assessment of the behaviour of passive de-adoption was performed through a retrospective longitudinal observational study based upon administrative hospital data. Results A comprehensive conceptual model that takes an integrated approach to de-adoption was assembled from lessons learnt when dealing with low value care previously. It identified three stages in the de-adoption cycle which are necessary for success: identification, implementation and re-evaluation. Each process should be performed at multiple planes: national (macro), local (meso) and provider / patient (micro) levels in order to have a holistic effect. The identification of low value interventions may be from exploring peer reviewed literature, as demonstrated from the systematic review or exploring geographical variation of care. Said review identified 71 low value procedures, of which 5 interventions which carried the highest economic burden were postulated to cost the health system £135 million per annum. Subsequent granular review identified that passive levers have not resulted in de-adoption of a surgical low value interventions – e.g. delayed cholecystectomy. This is due to the presence of exnovator providers whom are concurrently de-adopting innovative interventions as other providers are adopting them. Conclusions Low value care represents a significant burden in the current health service. This thesis has evaluated its incidence and economic burden in general surgery. Service transformation is necessary and may be achieved through the holistic integrated approach recommended here. Policy makers have already sought this novel information and encompassed it into national policy, with the objective of achieving higher value care through effective de-adoption.Open Acces