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.

Tutorial topics:

Machine learning for the next generation of health informatics

Nov. 10, 11am EST
Dr Huiqi (Yvonne) Lu and Dr Samaneh Kouchaki, The Institute of Biomedical Engineering – Oxford University

Abstract: 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.

From Raw Data to Prognosis: A Hands-on Tutorial

Nov. 10, 2pm EST
Dr Matteo Corbetta and George Gorospe, KBR @ NASA Ames Research Center

Abstract: This tutorial will focus on the fundamentals and basic concepts of prognostics and health management, giving emphasis to condition-based approaches. The audience will be introduced to the key elements that compose a prognostic framework, their interaction, uncertainty and effect on the prediction of the system evolution over time. The session will continue with an overview of data-driven and model-based approaches for prognostics, and will also propose two case studies on prognostic and failure prediction written in Python programming language. The participants will have direct access to the Python scripts and will be able to run them on their personal laptop*. The tutorial will summarize the theory behind the two algorithms, and will guide the audience through the code for a thorough understanding, from data preprocessing to output representation.

*The examples will require Python 2.6 or later, and libraries NumPy, SciPy, and matplotlib, installed on the machine.

prognostics.zip273.46 KBDecember 10, 2020 - 8:21pm

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