BrainX Community Live!
BRAINX COMMUNITY, a group of experts in machine learning, healthcare and innovation has grown to be 1200+ international member strong since its inception in May 2018.
Our June 2019,BrainX community Live event is titled, "Solving healthcare challenges with machine learning and artificial intelligence".This session is being jointly organized by BrainX Community and Center for Computational Imaging and Personalized Diagnostics,Case Western Reserve University.Dr. Ashish Khanna, Dr. Rakesh Shiradkar and Professor Sharona Hoffman,(see Bios below) will discuss multifaceted challenges facing healthcare and the promise of machine learning and artificial intelligence in solving them. Unique aspects of ethical and legal issues with application of artificial intelligence in healthcare will also be addressed during this session.
For more details, BrainX Community platform is available through https://www.brainxai.org and LinkedIn group called BrainX Community. No registration, no fees, refreshments provided and open invite to all! Please feel free to share with friends and colleagues.
BRAINX COMMUNITY LIVE! PROGRAM
June 11, 2019
Location:Nord 356,Case Western Reserve University campus
2095 Martin Luther King Jr Dr, Cleveland, OH 44106
5:00-5:05 PM: BrainX Community live!
5:05-6:00 PM:"Solving healthcare challenges with machine learning and artificial intelligence".
Clinical challenges: Ashish K. Khanna MD,FCCP, FCCM.
Machine learning challenges: Rakesh Shiradkar, PhD.
Ethical issues: Professor Sharona Hoffman
6:00-6:30 PM: Discussion and Networking session.
Parking directions: Best place to park is the Veale parking garage, which is at 2158 Adelbert Rd. From the parking garage, walk west to the opposite side of the football field, turn right, and Nord Hall is on the left.
Rakesh Shiradkar, PhD.
Dr. Shiradkar’s research focuses on developing machine learning and artificial intelligence methods in conjunction with quantitative and conventional MRI for image based diagnosis and prognosis of prostate cancer. Non – invasive imaging based diagnosis can potentially aid in not only improving characterization of cancer on imaging, but also alleviate issues arising out of conventional invasive practices such as biopsies and blood based tests. His research is aimed at non-invasively identifying risk of prostate cancer progression, response to therapies and treatment outcomes. He is also working on integrating multi-modal imaging and clinical data such as MRI, pathology, genomics for developing reliable and robust methods for cancer diagnostics.
Professor Sharona Hoffman
Sharona Hoffman is the Edgar A. Hahn Professor of Law, Professor of Bioethics, and Co-Director of the Law-Medicine Center at Case Western Reserve University. She received a B.A. from Wellesley College, J.D. from Harvard Law School, LL.M. in health law from the University of Houston, and S.J.D. from Case Western Reserve University.
Dr. Hoffman has won numerous awards for her teaching and scholarship, including the Law School’s Distinguished Research Award in 2016. She was elected to membership in the American Law Institute in 2017.
Professor Hoffman has published over 60 substantial law journal articles and book chapters and dozens of shorter pieces. Her work focuses on health law and civil rights topics and has appeared in prestigious publications such as the Georgetown Law Journal, Harvard Journal of Law and Technology, Yale Journal of Health Policy, Law, and Ethics, and many other journalss. She is also the author of two books: Aging with a Plan: How a Little Thought Today Can Vastly Improve Your Tomorrow (Praeger 2015) and Electronic Health Records and Medical Big Data: Law and Policy (Cambridge University Press 2016).
Professor Hoffman has lectured throughout the United States and internationally and has been widely quoted in the press. For more information see her website http://sharonahoffman.com/.
Ashish K. Khanna MD,FCCP,FCCM
Ashish K.Khanna, MD, FCCP, FCCM, is a staff intensivist & anesthesiologist, associate professor of anesthesiology and associate chief for research with the department of anesthesiology, section on critical care medicine at the Wake Forest University School of Medicine, Winston-Salem, NC.
His research interests include, prediction of post-operative respiratory and cardiac events on the regular nursing floor, outcomes of hypotension in critically ill patients and use of novel vasopressors in shock states in the ICU. From 2015-2017 Dr.Khanna was the lead investigator for the Angiotensin II in High Output Shock (ATHOS-3) trial, that was subsequently published in the NEJM in the summer of 2017. He also recently lead and completed the Prediction of Opioid Induced Respiratory Depression in Patients Monitored by Capnography (PRODIGY) trial in 16 sites across the world. Being involved in the big data revolution, Dr.Khanna has partnered with other experts in developing the Artificial Intelligence Diagnosis Engine (AIDE) as part of a successful IBM Watson Artificial Intelligence XPrize effort. This platform will help clinicians generate and document diagnosis specific codes in an effective and efficient manner (www.BRAINXai.com) He has more than 50 peer reviewed papers, two dozen book chapters, editorials, invited non-peer reviewed articles, and online educational videos to his credit and has been invited to talk about his work at prestigious national and international forums.
A simple but exclusive 14 step guide compiled by members of BrainX Community to help get started with machine learning in healthcare.
Learning the basic programming skills, resources needed to get started on a project and more are all outlined in this simple guide.
Most importantly, it's a primer in getting bilingual and fostering collaboration between healthcare professionals and machine learning experts.
Facts and Figures
1200+ international members:Largest online community for machine learning in healthcare.
12 Live monthly monthly sessions since May 2018.
Largest repository of data sources links.
Single place for curated machine learning in healthcare scientific articles.
Diversity in membership including Medical Students, Computer Science Graduate and Postgraduate Students, Physicians, Data Scientists, AI/ML Academicians, University Professors, Inventors, Innovation Managers, Entrepreneurs and even High School Students!
Machine Learning in healthcare for good.
Connect, learn and share scientific knowledge about applications of Machine Learning in healthcare.
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