This book constitutes the thoroughly refereed proceedings of the First International Conference on InformationTechnology in Bio- and Medical Informatics, held in Bilbao, Spain, in September 2010. The 14 selected long papers and 8 selected short papers are divided in the following groups:Workflow management and database; Decision support and data management in biomedicine; Medical data modelling and information retrieval; Data mining in bioinformatics; Knowledge representation and data management in bioinformatics; Biological data and signal processing.

This book constitutes the refereed proceedings of the 8th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2017, held in Lyon, France, in August 2017. The 3 revised full papers and 6 poster papers presented were carefully reviewed and selected from 15 submissions. The papers address a broad range of topics in applications of information technology to biomedical engineering and medical informatics.

This book constitutes the refereed proceedings of the 7th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2016, held in Porto, Portugal, in September 2016, in conjunction with DEXA 2016. The 9 revised long papers presented together with 11 poster papers were carefully reviewed and selected from 26 submissions. The papers address the following topics: biomedical data analysis and warehousing; information technologies in brain science; and social networks and process analysis in biomedicine.

This book constitutes the refereed proceedings of the Third International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2012, held in Vienna, Austria, September 2012, in conjunction with DEXA 2012. The 12 revised long papers and 3 revised short papers presented were carefully reviewed and selected from numerous submissions. The papers address the following topics: medical data mining and information retrieval; metadata models, prediction and mobile applications; systems biology and data mining in bioinformatics. The papers show how broad the spectrum of topics in applications of information technology to biomedical engineering and medical informatics is.

U-Healthcare Monitoring Systems: Volume One: Design and Applications focuses on designing efficient U-healthcare systems which require the integration and development of information technology service/facilities, wireless sensors technology, wireless communication tools, and localization techniques, along with health management monitoring, including increased commercialized service or trial services. These u-healthcare systems allow users to check and remotely manage the health conditions of their parents. Furthermore, context-aware service in u-healthcare systems includes a computer which provides an intelligent service based on the user's different conditions by outlining appropriate information relevant to the user's situation. This volume will help engineers design sensors, wireless systems and wireless communication embedded systems to provide an integrated u-healthcare monitoring system. This volume provides readers with a solid basis in the design and applications of u-healthcare monitoring systems. - Provides a solid basis in the design and applications of the u-healthcare monitoring systems - Illustrates the concept of the u-healthcare monitoring system and its requirements, with a specific focus on the medical sensors and wireless sensors communication - Presents a multidisciplinary volume that includes different applications of the monitoring system which highlight the main features for biomedical sensor devices

Mechanizing hypothesis formation is an approach to exploratory data analysis. Its development started in the 1960s inspired by the question “can computers formulate and verify scientific hypotheses?”. The development resulted in a general theory of logic of discovery. It comprises theoretical calculi dealing with theoretical statements as well as observational calculi dealing with observational statements concerning finite results of observation. Both calculi are related through statistical hypotheses tests. A GUHA method is a tool of the logic of discovery. It uses a one-to-one relation between theoretical and observational statements to get all interesting theoretical statements. A GUHA procedure generates all interesting observational statements and verifies them in a given observational data. Output of the procedure consists of all observational statements true in the given data. Several GUHA procedures dealing with association rules, couples of association rules, action rules, histograms, couples of histograms, and patterns based on general contingency tables are involved in the LISp-Miner system developed at the Prague University of Economics and Business. Various results about observational calculi were achieved and applied together with the LISp-Miner system. The book covers a brief overview of logic of discovery. Many examples of applications of the GUHA procedures to solve real problems relevant to data mining and business intelligence are presented. An overview of recent research results relevant to dealing with domain knowledge in data mining and its automation is provided. Firsthand experiences with implementation of the GUHA method in the Python language are presented.

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.