The problem of variable selection for financial distress: applying GRASP methaeuristics

We use the GRASP procedure to select a subset of financial ratios that are then used to estimate a model of logistic regression to anticipate financial distress on a sample of Spanish firms. The algorithm we suggest is designed "ad-hoc" for this type of variables. Reducing dimensionality has several advantages such as reducing the cost of data acquisition, better understanding of the final classification model, and increasing the efficiency and the efficacy. The application of the GRASP procedure to preselect a reduced subset of financial ratios generated better results than those obtained directly by applying a model of logistic regression to the set of the 141 original financial ratios.Genetic algorithms, Financial distress, Failure, Financial ratios, Variable selection, GRASP, Metaheuristic

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

The risk of re-intervention after endovascular aortic aneurysm repair

This thesis studies survival analysis techniques dealing with censoring to produce predictive tools that predict the risk of endovascular aortic aneurysm repair (EVAR) re-intervention. Censoring indicates that some patients do not continue follow up, so their outcome class is unknown. Methods dealing with censoring have drawbacks and cannot handle the high censoring of the two EVAR datasets collected. Therefore, this thesis presents a new solution to high censoring by modifying an approach that was incapable of differentiating between risks groups of aortic complications. Feature selection (FS) becomes complicated with censoring. Most survival FS methods depends on Cox's model, however machine learning classifiers (MLC) are preferred. Few methods adopted MLC to perform survival FS, but they cannot be used with high censoring. This thesis proposes two FS methods which use MLC to evaluate features. The two FS methods use the new solution to deal with censoring. They combine factor analysis with greedy stepwise FS search which allows eliminated features to enter the FS process. The first FS method searches for the best neural networks' configuration and subset of features. The second approach combines support vector machines, neural networks, and K nearest neighbor classifiers using simple and weighted majority voting to construct a multiple classifier system (MCS) for improving the performance of individual classifiers. It presents a new hybrid FS process by using MCS as a wrapper method and merging it with the iterated feature ranking filter method to further reduce the features. The proposed techniques outperformed FS methods based on Cox's model such as; Akaike and Bayesian information criteria, and least absolute shrinkage and selector operator in the log-rank test's p-values, sensitivity, and concordance. This proves that the proposed techniques are more powerful in correctly predicting the risk of re-intervention. Consequently, they enable doctors to set patients’ appropriate future observation plan

Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) represents one of the most significant global health issues, given its high prevalence and the challenging nature and physiology of the liver and hepatic surgery, in its many forms. This means that the most appropriate management for HCC should incorporate a multidisciplinary approach, combining the expertise from several different specialties. This book showcases the various steps in the development, diagnosis, staging, and management of HCC and provides views and thoughts from true experts in the field. As such, it is a useful resource for any physician or surgeon, whether training or practicing, who is interested in caring for patients with HCC

Sistema de Apoio à Análise e ao Tratamento de Doentes com Carcinoma Hepatocelular

Dissertação de Mestrado Integrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.O Cancro do figado é o sexto cancro mais frequentemente diagnosticado e a terceira causa de morte por doenças relacionadas com cancro em todo o Mundo. O Carcinoma Hepatocelular (CHC) está na origem de mais de 90% dos tumores primários do figado, sendo considerado um problema à escala global. As guidelines clinicas, suportadas pela Medicina Baseada na Evidência (MBE), procuram auxiliar os clínicos no seu processo de tomada de decisão. No entanto, a prática clínica lida frequentemente com o desfasamento entre a MBE e a desejada Medicina Personalizada (MP), ajustada a um dado doente. De modo a poderem tomar decisões fundamentadas, os clínicos necessitam de ter a informação dos doentes disponível para consulta, a qualquer altura. Na maioria dos contextos hospitalares, a informação clínica do doente está muitas vezes registada em suporte físico (papel), distribuída por várias instalações. Isto torna os ficheiros igualmente susceptíveis a dados em falta. Neste trabalho, apresentamos um Sistema de Apoio à Decisão Clínica, para a gestão de dados clínicos de doentes com CHC. É também apresentado um módulo de Inteligência Artificial a ser integrado no sistema. Váarios métodos de análise de agrupamentos foram utilizados de modo a determinar grupos prognósticos com diferentes características, considerando dados heterogéneos e com valores em falta. A análise propiciou a divisão em dois grandes grupos, G1 e G2, com sobrevivências globais estatisticamente signifícativas. Os nossos resultados sugerem igualmente uma heterogeneidade entre os doentes no estádio avancado da doença. Foram ainda avaliados alguns métodos de classificação, de modo a desenvolver modelos preditivos para a atribuição do grupo mais correcto para um determinado doente. Em resumo, este trabalho foca-se no desenvolvimento de uma ferramenta que alie a gesão de dados clínicos a um "motor inteligente" de inferência que permita gerar recomendações uteis aos clínicos nas suas actividades diárias. O sistema integra algoritmos de Inteligência Artificial que permitem orientar os tratamentos dos doentes no âmbito da Medicina Personalizada. Palavras-Chave : Carcinoma Hepatocelular (CHC), Medicina Baseada na Evidência (MBE), Medicina Personalizada (MP), Preenchimento de dados em falta, Sistema de Apoio à Decisão Clínica (SADC), Personalização de Grupos Prognósticos, Métodos de Agrupamento, Inteligência Artificial (IA), dados clínico

Hepatic Surgery

Longmire, called it a "hostile" organ because it welcomes malignant cells and sepsis so warmly, bleeds so copiously, and is often the ?rst organ to be injured in blunt abdominal trauma. To balance these negative factors, the liver has two great attributes: its ability to regenerate after massive loss of substance, and its ability, in many cases, to forgive insult. This book covers a wide spectrum of topics including, history of liver surgery, surgical anatomy of the liver, techniques of liver resection, benign and malignant liver tumors, portal hypertension, and liver trauma. Some important topics were covered in more than one chapter like liver trauma, portal hypertension and pediatric liver tumors