Applying Machine Learning Algorithms to Predict Endometriosis Onset

Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life

Criação de interfaces gráficas automatizadas, dinâmicas e adaptáveis

O ambiente corporativo enquadrado nas telecomunicações conjugado com o aparecimento de novos serviços é cada vez mais complexo e com mudanças/transformações mais aceleradas. O negócio e os sistemas de suporte necessitam de evoluir e adaptarem-se de forma muito rápida para esta nova realidade e exigência. Deste modo, a criação de aplicações adaptáveis e contextualizadas, proporciona interações mais ricas, permitindo que as interfaces entreguem a informação da melhor forma possível, com uma melhor experiência de utilização. Esta dissertação tem como principal objetivo a definição e a implementação de metodologias para gerar interfaces gráficas dinâmicas em tempo real e adaptáveis de acordo com os comportamentos do utilizador. Estas metodologias de Inteligência Artificial, passam pela utilização de técnicas de Machine Learning. Os resultados finais permitiram obter uma interface adaptável e dinâmica a cada utilizador, sendo esta gerada em tempo real.The corporative environment framed in telecommunications combined with the emergence appearance of new services is increasingly complex and with fast transformations. This new reality and its requirements demand that the business and the support systems evolve and quickly adapt to it. As a result, the creation of an adaptive and contextualized application provides richer interactions, allowing interfaces to offer the information in the best possible way, delivering a better user experience. The main objective of this dissertation is to define and implement methodologies to generate dynamic and adaptable interfaces according to the user behavior, in realtime. These Artificial Intelligence methodologies are developed using techniques of Machine Learning. The final results made it possible to obtain a dynamic and adaptable interface for each user in realtime.Mestrado em Engenharia Informátic

A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition

Hierarchical Concatenation Networks (HCN) are inspired by the way humans recognize patterns; i.e. by concatenating small features. In HCNs patterns are split into small parts, and then concatenated and activated in the network’s layers. The research in this thesis investigated and explored feature extraction methods, similarity measures, and classification using HCNs. Results indicate that HCNs can be used in automatic pattern recognition systems with better performance rate on the lower layer than the top layer

Endometriosis

Endometriosis is a common and serious disease that is estimated to cost the world economy $9.7 billion a year. Most of these costs come from lost productivity at work. As such, it is important to help women receive earlier diagnosis and more effective treatment. This book presents a comprehensive overview of endometriosis, including information on molecular diagnostics and imaging methods for early detection as well as new, less-invasive treatments that preserve women’s fertility