Alex Esteves Jaccoud Falcão

Application of the Intelligent Techniques in Transplantation Databases: A Review of Articles Published in 2009 and 2010

The replacement of defective organs with healthy ones is an old problem, but only a few years ago was this issue put into practice. Improvements in the whole transplantation process have been increasingly important in clinical practice. In this context are clinical decision support systems (CDSSs), which have reflected a significant amount of work to use mathematical and intelligent techniques. The aim of this article was to present consideration of intelligent techniques used in recent years (2009 and 2010) to analyze organ transplant databases. To this end, we performed a search of the PubMed and Institute for Scientific Information (ISI) Web of Knowledge databases to find articles published in 2009 and 2010 about intelligent techniques applied to transplantation databases. Among 69 retrieved articles, we chose according to inclusion and exclusion criteria. The main techniques were: Artificial Neural Networks (ANN), Logistic Regression (LR), Decision Trees (DT), Markov Models (MM), and Bayesian Networks (BN). Most articles used ANN. Some publications described comparisons between techniques or the use of various techniques together. The use of intelligent techniques to extract knowledge from databases of healthcare is increasingly common. Although authors preferred to use ANN, statistical techniques were equally effective for this enterprise.

Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content

INTRODUCTION Internet users are increasingly using the worldwide web to search for information relating to their health. This situation makes it necessary to create specialized tools capable of supporting users in their searches. OBJECTIVE To apply and compare strategies that were developed to investigate the use of the Portuguese version of Medical Subject Headings (MeSH) for constructing an automated classifier for Brazilian Portuguese-language web-based content within or outside of the field of healthcare, focusing on the lay public. METHODS 3658 Brazilian web pages were used to train the classifier and 606 Brazilian web pages were used to validate it. The strategies proposed were constructed using content-based vector methods for text classification, such that Naive Bayes was used for the task of classifying vector patterns with characteristics obtained through the proposed strategies. RESULTS A strategy named InDeCS was developed specifically to adapt MeSH for the problem that was put forward. This approach achieved better accuracy for this pattern classification task (0.94 sensitivity, specificity and area under the ROC curve). CONCLUSIONS Because of the significant results achieved by InDeCS, this tool has been successfully applied to the Brazilian healthcare search portal known as Busca Saúde. Furthermore, it could be shown that MeSH presents important results when used for the task of classifying web-based content focusing on the lay public. It was also possible to show from this study that MeSH was able to map out mutable non-deterministic characteristics of the web.

Artificial Intelligence Techniques: Predicting Necessity for Biopsy in Renal Transplant Recipients Suspected of Acute Cellular Rejection or Nephrotoxicity

The gold standard for nephrotoxicity and acute cellular rejection (ACR) is a biopsy, an invasive and expensive procedure. More efficient strategies to screen patients for biopsy are important from the clinical and financial points of view. The aim of this study was to evaluate various artificial intelligence techniques to screen for the need for a biopsy among patients suspected of nephrotoxicity or ACR during the first year after renal transplantation. We used classifiers like artificial neural networks (ANN), support vector machines (SVM), and Bayesian inference (BI) to indicate if the clinical course of the event suggestive of the need for a biopsy. Each classifier was evaluated by values of sensitivity and area under the ROC curve (AUC) for each of the classifiers. The technique that showed the best sensitivity value as an indicator for biopsy was SVM with an AUC of 0.79 and an accuracy rate of 79.86%. The results were better than those described in previous works. The accuracy for an indication of biopsy screening was efficient enough to become useful in clinical practice.

Uso da ferramenta PreText para mineração de textos extraídos do NCBI para estudo epistemológico da Informática em Saúde

Este artigo apresenta a utilizacao da ferramenta PreText como tecnica auxiliar a uma pesquisa sobre epistemologia da Informatica em Saude (IS), que visa inferir se a IS se caracteriza como ciencia, tecnologia, tecnociencia ou arte. O PreText tem por objetivo realizar pre-processamento de textos, transformando-os em um formato estruturado, usando a abordagem bag-of-words, e foi aplicado aos metadados de 437.289 resumos de artigos cientificos extraidos da base PubMed Central. Os resultados do processamento foram exportados para uma base de dados e relacionados a um colecao de termos de um tesauro especializado em IS construido pelos autores, denominado EpistemIS, e aos metadados dos artigos para geracao de estatisticas. Tais relacoes possibilitaram compreender a epistemologia da IS, inferindo que esta e uma tecnociencia interdisciplinar que atua nos dominios das Ciencias da Vida, Ciencias da Saude e Cuidado em Saude.