 The 7th Big Data Analytic Technology For Bioinformatics and Health Informatics Workshop (KDDBHI 2020) In conjunction with 2020 IEEE International Conference on Big Data (IEEE BigData 2020)  Workshop Schedule (EST Time) on December 10th 9:20am – 9:30am Opening remark, Dr. Xin Deng (Chair of KDDBHI Workshop) 9:30am –10:30am Keynote Speech: Graph-based Representation Learning For Electronic Health Records, Prof. Edward Choi 10:30am – 10:50am Assessing SARS-CoV-2 Spike Protein Flexibility for Potential Therapeutic Targets with a Combined Simulation and Deep Learning Approach, Serena Chen 10:50am – 11:10am Multi-label Detection and Classification of Red Blood Cells in Microscopic Images, Jiaming Guo 11:10am – 11:30am Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text, Mohammed Alawad 11:30am – 11:50am Perception detection using Twitter, Vanja Ljevar 11:50am – 12:10pm Coffee Break 12:10pm – 12:30pm A Prototype Application to Identify LGBT Patients in Clinical Notes, Terri Elizabeth Workman 12:30pm – 12:50pm Contrast-resolution Evaluation of Fourier Based High Frame Rate Imaging, Zhaohui Wang 12:50pm – 13:10pm Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information, Tunazzina Islam 13:10pm – 13:30pm A Deep Recurrent Neural Network to Support Guidelines and Decision Making of Social Distancing, Mohammed Aledhari
Workshop Introduction and Motivation This will be the seventh annual KDDBHI workshop continuing on the success of prior six workshops. The length of the workshop is half day. The first KDDBHI workshop is debuted at KDD 2014, New York, NY. The 2nd KDDBHI workshop is held in conjunction with ACM BCB 2015 conference, Atlanta, GA. The 3rd to 6th KDDBHI workshop are held in conjunction with IEEE Big Data 2016, 2017, 2018 and 2019 Conferences. In the seventh workshop, we intend to continue on the success of 6 prior workshops, and exchange, share, facilitate and promote research and applications of advanced analytics, machine learning, deep learning and transfer learning in healthcare, health informatics and bioinformatics, e.g. precision medicine and personalized medicine, in particular, the integration and fabrication of big data from patient genetic sequencing, patient life style and socio-economic data, structured and unstructured medical records, drug design and targeting, and population and individual level drug treatment, and medical and pharmacy claims, etc. The application focus will also include the applications of such in the pandemic of COVID-19. The information about prior workshops can be found at http://www.kddbhi.org. The emerging fusion of Bioinformatics and Health Informatics has promoted research in target drug, personalized medicine, clinical decision support and population health management, etc. and collaborations among researchers in bioinformatics and health informatics and clinicians as well as data scientists. The recent advancements in deep learning and transfer learning promote a lot of applications in image diagnosis, genomics sequencing, EMR Medical Records, NLP with EMR text notes. The recent announcements of CMS AI challenge of predicting re-admissions will also attract more attention to this field. The goal of this workshop is to provide a forum for researchers, practitioners, clinicians, business stakeholders, and data scientists to exchange current research progress and direction, machine learning, deep learning, transferring in healthcare, data fusion and knowledge representation, so as to facilitate fusion of Bioinformatics, Health Informatics, Precision Medicine, and Population Health. Ultimately it contributes to better quality of life of patients and healthier population as well as big data analytic technology advancements that support and promote such research activities and applications. Workshop Topics and Target Audience As increasingly massive amounts of computational biological information, including genome sequences, protein sequences, gene expression data, becomes available, more efficient, sensitive, and specific big data analytic technology in Bioinformatics become critically in need. For example, terabytes or more of raw data are easily generated in next-generation sequencing experiments. Also, in biological and biomedical imaging process and analysis, large volumes of data are generated. Consequently, how to store, achieve, index, manage, learn, mine, and visualize the big data is clearly a challenge to the research community. Similarly, for the past decade, there have been a variety of efforts and progresses from healthcare organizations and companies in digitizing, storing, analyzing medical data. For instance, predictive analytics and risk adjustment allow insurance companies and healthcare organizations to predict the future costs for budgets and population health management, perform risk adjustment, develop the treatment guidelines, plan care management strategies, and measure physician performance.
With advancements of deep learning in recent years, it opens up new challenges, opportunities, research and applications in bioinformatics, heath informatics and healthcare delivery. With The emergence and advancements in Precision Medicine, it calls for further fusion of Bioinformatics, Health Informatics, Clinical Outcome and Population Health, and application of big data analytics to the ever growing big data. For instance, the integration of genetic test results, patient-specific sequencing, expression profiling, tissue image data, and clinical data in a patient medical record provides opportunities for personalized medicine, target drug research, and treatment effectiveness research, which are all necessary components of precision medicine. The recent advancement in big data technology and research, make it now the perfect time to marry the research and application. · Big Data and AI Applications in fighting COVID-19 Pandemic · Fusion of Data and Knowledge Representation of HER records and other data sources · Deep Learning Research and Application in Healthcare · Natural Language Processing and Text Mining with HER records · Integrated Platform for Deep Learning in Healthcare · Medical Image Processing, Storage and Diagnosis · Successful Use Cases of Deep Learning and NLP in Healthcare · Precision Medicine & Personalized Medicine · Health Analytics and Informatics · Target Drug Design and Discovery · RNAseq and Microarray Gene expression Data Analysis · Gene Regulatory Network Construction · Next-generation Sequencing (NGS) Analysis · Functional Genomics · Population, Evolution, and Comparative Genomics · Transnational Bioinformatics · Protein Structure Prediction · Protein Function Analysis · Healthcare and Healthcare Delivery · Healthcare policy research · Healthcare outcomes research, monitoring and evaluation · Hospital Information System · Electronic Medical Record and Electronic Health Record · Population Health and Public Health Management · Mobile Health and Sensor Applications · Digital Health · Other areas related to healthcare informatics and analytics
· Other areas related to proteomics and genomics Chairs & Primary Contact Dr.Xin Deng, PhD, Senior Machine Learning Scientist, Microsoft Corporation, Redmond, WA, xinde@microsoft.com
Dr.Donghui Wu, PhD, MBA, Lead Data Scientist/Head, Advanced Analytics and Artificial Intelligence, Texas Health Resources, donghui.wu@ieee.org, 321-946-0648 Steering Committee Donghui Wu, Lead Data Scientist at Texas Health Resources, Arlington, TX Dongseong Hwang, AI Software Engineer, Google Paul Bradley, Chief Data Scientist at ZirMed, Chicago, Illinois Jianlin Cheng, Distinguished Professor, Computer Science Department, Informatics Institute, C. Bond Life Science Center, University of Missouri- Columbia, Columbia, MO Xin Deng, Senior Machine Learning Scientist, Microsoft Corporation, Redmond, WA Aidong Zhang, Professor of Computer Science and Biomedical Engineering, University of Virginia, Richmond, VA Mohammed J. Zaki, Professor, Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY Senior Program Committee Xin Deng, Machine Learning Scientist, Microsoft Corporation, Redmond, WA Dongseong Hwang, AI Software Engineer, Google Junhua Ding, Professor of Data Science, University of North Texas, Denton, TX Donghui Wu, Lead Data Scientist at Texas Health Resources, Arlington, TX Weiwei Ouyang, Senior Data Scientist at UnitedHealth Group, Hopkins, Minnesota Terri Workman, the Biomedical Informatics Center, George Washington University Lixia Yao, Associate Professor at Mayo Clinic Rochester, Minnesota TIMELINE • Oct 30, 2020: Due date for full workshop papers submission • Nov 15, 2020: Notification of paper acceptance to authors • Nov 20, 2019: Camera-ready of accepted papers
• Dec 10-13 2020: Workshops
|
