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|12/15/21||Dr. Byung-Jun Yoon, Associate Professor, Electrical and Computer Engineering, Texas A&M University||Machine Learning for Computational Network Biology||Click here for the recording.|
|1/14/22||Dr. Jessilyn Dunn, Assistant Professor, Department of Biomedical Engineering, Duke University||The Digital Physiome: Wearables for Early Disease Detection||Click here for the recording.|
|2/11/22||Dr. Stephen T.C. Wong, The John S. Dunn, Sr. Distinguished Endowed Chair in Biomedical Engineering
Professor, Radiology, Neurosciences, Pathology,
Houston Methodist Hospital and Weill Cornell Medical College
|The Possibility of Cognitive Automation in Medicine.||Click here for the recording.|
|4/29/2022||Dr. Yanshan Wang, Assistant Professor, Dept. of Health Informatics Management, University of Pittsburgh||From Clinical Language Representation to Patient Representation using Electronic Health Records||Click here for the recording.|
|5/27/2022||Dr. Shankar Subramaniam, Professor, Bioengineering, University of California San Diego||Alzheimer’s Disease: At the Interface of Engineering, Medicine, and Biology||Click here for the recording.|
May 27, 12pm ET
Dr. Shankar Subramaniam, Professor, Bioengineering, University of California San Diego
Alzheimer’s Disease: At the Interface of Engineering, Medicine, and Biology
Abstract: Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with development of novel engineering methods and technologies. In this talk I will focus on systems and engineering approaches to a major health care need of diagnosis and treatment of Alzheimer’s disease. Alzheimer’s disease is a debilitating disease that afflicts six million people in the United States and has no successful treatment. While cognitive measurements are often the diagnostic indicators followed by various live brain imaging, the diagnoses come too late and clinical trials over two decades on treatments have failed. In this talk, I will identify the challenges in investigating Alzheimer’s disease and outline how recent technologies and analytics strategies can help in paving the way for early diagnosis and in designing novel treatments.
Apr. 29, 12pm ET
Dr. Yanshan Wang, Assistant Professor, Dept. of Health Informatics Management, University of Pittsburgh
From Clinical Language Representation to Patient Representation using Electronic Health Records
Abstract: The widespread adoption of Electronic Health Records (EHRs) has enabled the use of clinical data for clinical research and practice. Since a significant portion of relevant patient information is embedded in clinical narratives, natural language processing (NLP) techniques such as information extraction have become critical for using EHRs in clinical research. Meanwhile, encoding entire EHR data in a patient representation has shown promising results in predictive modeling, and could help clinical decision making and facilitate translational research. This talk will feature clinical NLP methodologies and applications, and discuss why learning better representations from EHRs is crucial for clinical and translational research.
Biosketch: Dr. Yanshan Wang is an Assistant Professor and Vice Chair of research with a primary appointment in the Department of Health Information Management, School of Health and Rehabilitation Sciences, and a secondary appointment in the Intelligent Systems Program, School of Computing and Information, at the University of Pittsburgh. His research interests focus on artificial intelligence (AI), natural language processing (NLP), and machine learning methodologies and applications in health care. His research goal is to leverage different dimensions of data and data-driven computational approaches to meet the needs of clinicians, researchers, and patients. He joined Pitt in June 2021 from the Mayo Clinic where he still holds an adjunct Assistant Professor position.
Feb. 11, 12pm ET
Dr. Stephen Wong
The John S. Dunn, Sr. Distinguished Endowed Chair in Biomedical Engineering Professor, Radiology, Neurosciences, Pathology
Houston Methodist Hospital and Weill Cornell Medical College
The Possibility of Cognitive Automation in Medicine
The Healthcare industry in the United States is under digital transformation to address several macroeconomic and socioeconomic challenges including high costs and low quality, increase in federal regulations and policies, better expectation on patient experience, health inequities, hospital consolidation, and so on. Other developed and developing countries are facing similar healthcare transformation issues with different extents. Healthcare waste alone constitutes 25-30% or about $1 trillion of the healthcare spending in the United States. To increase efficiency and accessibility, artificial intelligence (AI) has recently been used in automating business, administrative, and operational processes in eliminating mundane, repetitive tasks performed by humans. But can we use AI to address the remaining tasks that involve large amounts of data and require human cognitive capabilities to perform non-routine tasks and enhance human decision-making performance?
In this discussion, we will present a use case on investigating and applying cognitive automation in stroke detection and diagnosis in our health system, involving convergence of AI technologies in natural language processing, speech analytics, medical image computing, data mining, machine learning, computer vision, visualization, and edge computing. We hope to engage a meaningful conversation on the possibility of cognitive automation in medicine. Do we need new scientific advances in AI like deep learning for cognitive automation? If cognitive automation is successful in medicine, what would be the future role of physicians? What happens to the patient-physician relationship and patient experience?
Dr. Stephen T.C. Wong, PhD, PE (FIEEE, FAMIA, FAIMBE, FAAIA) holds the John S. Dunn Sr. Presidential Chair and is the founding Chair of Systems Medicine and Bioengineering Department, Director of the T.T. & W.F. Chao Center for BRAIN, Director of Translational Biophotonics Laboratory, Chief of Medical Physics, and Associate Director of Cancer Center, Houston Methodist Hospital. He is a Professor of Radiology, Neurosciences, Pathology and Laboratory of Weill Cornell Medical College. Previously, he was a Professor at UCSF and Harvard University, handling major biomedical information and imaging system design and implementation at UCSF, Harvard Medical School and the Brigham and Women’s Hospital. Stephen has served in executive roles in major technology-driven companies including HP, AT&T Bell Labs, Philips Healthcare where his group implemented the largest radiology information systems in Europe, and Charles Schwab, where his group produced an electronic trading platform. His laboratory investigates molecular mechanisms of cancer and neurological disorders and translates findings into diagnostics and therapeutics. Wong received senior executive education from Stanford University, MIT, and Columbia University and is a licensed professional engineer (PE) in EE. He co-founded IEEE TC in BHI and helped revamp IEEE JBHI and launch IEEE BHI-BSN conferences. Stephen lived and worked in Hong Kong, Manila, Perth, Canberra, Singapore, Orlando, Tokyo, Amsterdam, San Francisco, Boston, and now Houston. He dedicates the second half of his life in solving disease problems.
Jan. 14, 12pm ET
Dr. Jessilyn Dunn,
Department of Biomedical Engineering
The Digital Physiome: Wearables for Early Disease Detection
Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health and wearable technologies. Recent technological advancements make it possible to closely and continuously monitor individuals using multiple measurement modalities in real time. We are collecting and integrating such wearables data with clinical information to gain a more precise understanding of health and disease and develop actionable, predictive health models for improving cardiometabolic and infectious respiratory disease outcomes. We are simultaneously developing open source data science and machine learning tools for the digital health community, including the Digital Biomarker Discovery Pipeline (DBDP), to facilitate the use of mobile device data in healthcare.
Dr. Jessilyn Dunn is Assistant Professor of Biomedical Engineering and Biostatistics & Bioinformatics at Duke University, and Director of the BIG IDEAs Laboratory whose goal is to detect, treat, and prevent chronic and acute diseases through digital health innovation. She is PI of the CovIdentify study to detect and monitor COVID-19 using mobile health technologies, and PI of a Chan Zuckerberg Initiative grant to develop the DBDP, an open-source software platform for digital biomarker development. Dr. Dunn was an NIH Big Data to Knowledge (BD2K) Postdoctoral Fellow at Stanford and an NSF Graduate Research Fellow at Georgia Tech and Emory, as well as a visiting scholar at the US Centers for Disease Control and Prevention and the National Cardiovascular Research Institute in Madrid, Spain. Her work has been internationally recognized with media coverage from the NIH Director’s Blog to Wired, Time, and US News and World Report.
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