学术活动

New direction in Machine Learning: Risk Predictions for Health Care

【主题】:New direction in Machine Learning: Risk Predictions for Health Care

【主讲】:Hamido FUJITA教授日本岩手县立大学


【时间】:9月7日(星期五)14点30分

【地点】:YF410



    1Hamido FUJITA教授简介

    He is professor at Iwate Prefectural University (IPU), Iwate, Japan, as a director of Intelligent Software Systems. He is the Editor-in-Chief of Knowledge-Based Systems, Elsevier of impact factor (4.528) for 2016. He received Doctor Honoris Causa from O’buda University in 2013 and also from Timisoara Technical University in 2018, and a title of Honorary Professor from O’buda University, Budapest, Hungary in 2011. He received honorary scholar award from University of Technology Sydney, Australia on 2012. He is Adjunct professor to Stockholm University, Sweden, University of Technology Sydney, National Taiwan Ocean University and others. He has supervised PhD students jointly with University of Laval, Quebec, Canada; University of Technology, Sydney, Australia; Oregon State University (Corvallis), University of Paris 1 Pantheon-Sorbonne, France and University of Genoa, Italy. He has four international Patents in Software System and Several research projects with Japanese industry and partners. He is vice president of International Society of Applied Intelligence, and Co-Editor in Chief of Applied Intelligence Journal, (Springer).He has given many keynotes in many prestigious international conferences on intelligent system and subjective intelligence.  He headed a number of projects including Intelligent HCI, a project related to Mental Cloning as an intelligent user interface between human user and computers and SCOPE project on Virtual Doctor Systems for medical applications.


2:报告简要内容

 

    Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making.

   The challenges in big data analyticsare the high dimensionality and complexity in data representationanalyticsespecially for on-line feature selection.  Granular computing and feature selectionon data streams are among the challenge to deal with big data analytics that is used for Decision making. We willdiscuss these challenges in this talk and provide new projection on ensembledeep learning techniquesfor on-line health care risk prediction. Different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to data analyticsdue to preprocessing and normalization processes which are expensive and difficult when data sets are raw, or imbalanced.  We will highlight these issues through project applied to health-care for elderly, by merging heterogeneous metrics from multi-sensing environment providing health care predictions assistingactive aging at home. We have utilized ensemble learning as multi-classification techniques on multi-data streams using incremental learning to update data change “concept drift“

  Subjectivity (i.e., service personalization) would be examined based on correlations between different contextualstructures that arereflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion.  Some of the attributes incompleteness also may lead to affect the approximation accuracy. I present deep learning feature selection in medical application early predictions (heart diseases and others). We outline issues on Virtual Doctor Systems, and highlights its innovation in interactions with elderly patients, also discuss these challenges in multiclass classification and decision support systems research domains. In this talk I will present the current state of art and focus it on health care risk analysis applications with examples from our experiments.

 

发布日期:2018-10-09  阅读次数:598次