One of the unique features of the PHM conferences is free technical tutorials on various topics in health management taught by industry experts. As educational events tutorials provide a comprehensive introduction to the state-of-the-art in the tutorial’s topic. Proposed tutorials address the interests of a varied audience: beginners, developers, designers, researchers, practitioners, and decision makers who wish to learn a given aspect of prognostic health management. Tutorials will focus both on theoretical aspects as well as industrial applications of prognostics. These tutorials reach a good balance between the topic coverage and its relevance to the community.
PHM Conference tutorials have been a popular event in the past and the PHM society is proud to continue this service to the community. Topics of interest of these tutorials span fundamentals of PHM (Diagnostics, Prognostics, Health Management, Uncertainty Management, etc.) as well as specialized topics such as Cost-Benefit analysis, Data-Mining, Electronics PHM, Bayesian Filtering for Prognosis, etc.
Steve King, IVHM Centre, Cranfield University, S.P.King@cranfield.ac.uk
The theme of the tutorial sessions for this PHM conference is Big Data and related analytics. The first presentation aims to provide an introduction to the topic, whilst the second opens the window into the domain of patient health care and the use of machine learning techniques. Although a different kind of health monitoring, it is hoped that the audience will see parallels between medical and mechanical asset health monitoring using advanced analytical methods.
The Power of Big Data and AI; Alchemy or Data Science – Dr Steve King, IVHM Centre – Cranfield University
Phrases such as Big Data, Cloud Computing and AI are now in everyday use and many people are now accessing cloud environments offered through broadband service providers for personal and company use. This talk will offer a personal view of how this technology has evolved over the past 15 years and provide an overview of some of the analytic capabilities used within Big Data environments and hopefully along the way demystify some of the concepts and terminology
Machine learning for the next generation of health informatics – Dr Huiqi (Yvonne) Lu and Dr Samaneh Kouchaki, The Institute of Biomedical Engineering – Oxford University
Healthcare systems worldwide are entering a new and exciting phase: ever-increasing quantities of clinical data are routinely collected, concerning all aspects of patient care, throughout the life of a patient. These Big Data in health and care are a unique combination of bacterial / viral genomics, noisy real-world clinical data, and many other data sources. Such analysis poses substantial challenges, including the high dimensionality of the data along, missing values, data heterogeneity, and scalability problems. Consequently, standard methods of medical data analysis are typically unable to handle data of this complexity. One of the most significant benefits of deploying machine learning methods is their ability to continually learn and improve from real-world experience (in data format). This is a key strength of machine learning, in which healthcare can move from reactive treatment to preventative medicine. As a result, innovations arising with machine learning approach can facilitate rapid clinical treatment, transform a hospital-only treatment pathway into a cost-effective home-based combined alternative, and improve the overall quality of health and care.