Data Mining in Clinical Medicine

This volume complies a set of Data Mining techniques and new applications in real biomedical scenarios. Chapters focus on innovative data mining techniques, biomedical datasets and streams analysis, and real applications. Written in the highly successful Methods in Molecular Biology series format, chapters are thought to show to Medical Doctors and Engineers the new trends and techniques that are being applied to Clinical Medicine with the arrival of new Information and Communication technologies

Authoritative and practical, Data Mining in Clinical Medicine seeks to aid scientists with new approaches and trends in the field.

Actigraphy Pattern Analysis For Outpatient Monitoring.- Definition of Loss Functions for Learning from Imbalanced Data to Minimize Evaluation Metrics.- Audit Method Suited for DSS in Clinical Environment.- Incremental Logistic Regression for Customizing Automatic Diagnostic Models.- Using Process Mining for Automatic Support of clinical Pathways Design.- Analyzing Complex Patients' Temporal Histories: New Frontiers in Temporal Data Mining.- The Snow System - A Decentralized Medical Data Processing System.- Data Mining for Pulsing the Emotion on the Web.- Introduction on Health Recommender Systems.- Cloud Computing for Context-Aware Enhanced m-Health Services.- Analysis of Speech-based Measures for Detecting and Monitoring Alzheimer's Disease.- Applying Data Mining for the Analysis of Breast Cancer Data .- Mining Data When Technology Is Applied To Support Patients and Professional On The Control Of Chronic Diseases: The Experience Of The METABO Platform For Diabetes Management.- Data Analysis In Cardiac Arrhythmias.- Knowledge-Based Personal Health System to Empower Outpatients of Diabetes Mellitus by means of P4 Medicine.- Serious Games For Elderly Continuous Monitoring.