The 6th International Workshop (KDDBHI 2019) on Big Data Analytic Technology for Bioinformatics and Health Informatics

Dec. 9th, 2019, Monday, 1:00 pm - 6:00 pm, San Bernardino Room

Westin Bonaventure Hotel & Suites located at 404 South Figueroa Street, Los Angeles, CA

Workshop Chairs: Donghui Wu, Xin Deng and T. Elizabeth Workman

1:00 – 1:10

Chairs’ Remarks

1:10 – 1:30

Explainable Deep Learning Applied to Understanding Opioid Use Disorder and Its Risk Factors

Terri Workman, Yijun Shao, Joel Kupersmith, Friedhelm Sandbrink, Joseph Goulet, Nawar Shara, Christopher Spevak, Cynthia Brandt, Marc Blackman, and Qing Zeng-Treitler

1:30 – 1:50

Towards Explainable Melanoma Diagnosis: Prediction of Clinical Indicators Using Semi-supervised and Multi-task Learning

Seiya Murabayashi and Hitoshi Iyatomi

1:50 – 2:10

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Xin He, Shihao Wang, Shaohuai Shi, Zhenheng Tang, Yuxin Wang, Ronghao Ni, Zhihao Zhao, Jing Dai, Xiaofeng Zhang, Xiaoming Liu, Zhili Wu, Wu Yu, and Xiaowen Chu

2:10 – 2:30

Stochastic Gastric Image Augmentation for Cancer Detection from X-ray Images

Hideaki Okamoto, Quan Huu Cap, Takakiyo Nomura, Hitoshi Iyatomi, and Jun Hashimoto

2:30 – 2:50

Automated Machine Learning for EEG-Based Classification of Parkinson's Disease Patients

Milan Koch, Victor Geraedts, Hao Wang, Martijn Tannemaat, and Thomas Bäck

2:50 – 3:10

Recurrent Neural Network Based Feature Selection for High Dimensional and Low Sample Size Micro-array Data

Shanta Chowdhury, Xishuang Dong, and Xiangfang Li

3:10 – 3:30

Exploiting Anti-Monotonic Constraint for Mining Palindromic Motifs from Big Genomic Data

Oluwafemi Sarumi and Carson Leung

3:30 – 3:40

Questions and Discussion

3:40 – 4:00

Coffee Break

4:00 – 4:20

Reinforcement Learning Framework to Identify Cause of Diseases - Predicting Asthma Attack Case

Quan Do, Alexa Doig, and Cao Son Tran

4:20 – 4:40

Bayesian Non-linear Support Vector Machine for High-Dimensional Data with Incorporation of Graph Information on Features

Wenli Sun, Changgee Chang, and Qi Long

4:40 – 5:00

Predicting Post-stroke Hospital Discharge Disposition Using Interpretable Machine Learning Approaches

Jin Cho, Alnour Alharin, Zhen Hu, Nancy Fell, and Mina Sartipi

5:00 – 5:20

Discovering Sublanguages in a Large Clinical Corpus through Unsupervised Machine Learning and Information Gain

Terri Workman, Guy Divita, and Qing Zeng-Treitler

5:20 – 5:40

Contrast-resolution Evaluation of Fourier Based High Frame Rate Imaging

Zhaohui Wang

5:40 – 6:00

Questions and Discussion

Closing Remarks

Workshop Introduction and Motivation

The emerging fusion of Bioinformatics and Health Informatics has promoted research and development in target drugs, 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. It also demands big data analytics incorporating latest advancements in artificial intelligence, data mining, machine learning, statistical methodology and big data. Precision Medicine and All People Health Initiatives promote more investment and research in precision medicine, an innovative approach to disease prevention and treatment that takes into account individual differences in people’s genes, environments, and lifestyles. In turn, it also creates higher demands for big data analytics for connecting genetic data, personal gene data, drug information, medical record, and clinical outcomes.

The goal of this workshop is to provide a platform for professionals, researchers, clinicians, and data scientists to share opinions and exchange ideas, so as to facilitate fusion of Bioinformatics, Health Informatics, Personalized Medicine, Population Health, Data Science and AI. 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.

This is the sixth annual KDDBHI workshop continuing on the success of three prior workshops. The First KDDBHI workshop was debuted at KDD 2014, New York, NY. The Second KDDBHI workshop is held in conjunction with ACM BCB 2015 conference, Atlanta, GA. The Third KDDBHI workshop is held at IEEE Big Data 2016 Conference, Washington, D.C. The fourth KDDBHI workshop is held at IEEE Big Data 2017 Conference, Boston, MA. The fifth KDDBHI workshop is held at IEEE Big Data 2018 Conference, Boston, MA. In this sixth workshop, we continue to attract more research and applications of big data analytics, data science and AI, technology and platforms for precision medicine, personalized medicine, and other clinical innovations, in particular, the integration and fabrication of big data from patient gene sequencing, patient life style and socio-economic data, electronic medical records, drug design and targeting, and population and individual level drug treatments, and medical and pharmacy claims as well as diagnostic imaging.

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 The emergence and advancements in Precision Medicine and Personalized 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.

· Fusion of Data and Knowledge Representation of EHR 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

Target Audience

The goal of this workshop is to bring together practitioners, researchers, clinicians, and data scientists in the area of Machine Learning, Deep Learning, Bioinformatics and Health Informatics to share latest findings in the field, exchange ideas on how to improve the strategies, address real-world problems in Bioinformatics and Healthcare, and explore the intersections between Big Data, Deep Learning, Bioinformatics and Health Informatics and new research areas brought by advancement in deep learning, big data analytics, data mining, machine learning and statistical learning.

Workshop Chairs

Donghui Wu, PhD, MBA, Advanced Analytics, Machine Learning and Artificial Intelligence, Dallas, TX, donghui.wu@ieee.org

Xin Deng, PhD, Senior Machine Learning Scientist , Microsoft Corporation, Redmond, WA, xinde@microsoft.com

T. Elizabeth Workman, PhD, Research Assistant Professor, Biomedical Informatics Center, George Washington University, School of Medicine and Health Sciences

Steering Committee

Dr. Donghui Wu, Lead Data Scientist at Texas Health Resources, Arlington, TX

Dr. Paul Bradley, Chief Data Scientist at ZirMed, Chicago, Illinois

Dr. Jianlin Cheng, Distinguished Professor, Computer Science Department, University of Missouri- Columbia, Columbia, MO

Dr. Xin Deng, Senior Machine Learning Scientist, Microsoft Corporation, Redmond, WA

Dr. Aidong Zhang, Professor of Computer Science and Biomedical Engineering, University of Virginia, Richmond, VA

Dr. Mohammed J. Zaki, Professor, Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY

Senior Program Committee

Dr. Xin Deng, Senior Machine Learning Scientist, Microsoft Corporation, Redmond, WA

Dr. Junhua Ding, Professor of Data Science, University of North Texas, Denton, TX

Dr. Vincent Emanuele, Manager of Data Sciences at Wellcentive, Atlanta, GA

Dr. Daisy Wang, Associate Professor, Director of Data Science Research Lab, Computer and Information Science and Engineering (CISE), University of Florida, Gainesville, FL

Dr. Donghui Wu, Lead Data Scientist at Texas Health Resources, Arlington, TX

Dr. Weiwei Ouyang, Senior Data Scientist at UnitedHealth Group, Hopkins, Minnesota

Dr. Lixia Yao, Associate Professor at Mayo Clinic, Rochester, Minnesota

TIMELINE

• Workshop Website and CFP: May 16, 2019

• Oct 28, 2019: Due date for full workshop papers submission

• Nov 10, 2019: Notification of paper acceptance to authors

• Nov 18, 2019: Camera-ready of accepted papers

• Dec 9-12 2019: Workshop and Conference [Note: workshop is most likely Dec. 9th]

Prior KDDBHI Workshops

2014 KDDBHI Workshop @ KDD 2014 The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2014, New Yrok, New York.

2015 KDDBHI Workshop @ ACM BCB 2015 The 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics , September 09 -12, 2015, Atlanta, GA.

2016 KDDBHI Workshop @ IEEE BigData2016 2016 IEEE International Conference on Big Data December 5 - December 8, 2016, Washington DC, USA

2017 KDDBHI Workshop @ IEEE BigData2017 2017 IEEE International Conference on Big Data December 11 - December 14, 2017, Boston, MA, USA

2018 KDDBHI Workshop @ IEEE BigData2018 2018 IEEE International Conference on Big Data December 9 - December 12, 2018, Seattle, WA, USA