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C16 The Development of a Machine Learning Model to Predict Risk of Inpatient Deterioration

Stephanie Skove
Bachelor of Science at Duke University
+ 12 speakers
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Machine Learning for Healthcare
August 8, 2020, Online, Los Angeles, CA, USA
Machine Learning for Healthcare
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About speakers

Stephanie Skove
Bachelor of Science at Duke University
Harvey Shi
Associate in Research at Duke Institute for Health Innovation (DIHI)
Ziyuan Shen
Software Engineer at ZipRecruiter
Michael Gao
Data Science Lead at Duke Institute for Health Innovation
Mengxuan Cui
Statistical Modeling Engineer at Yidu Tech Inc.
Marshall Nichols
Senior IT & Analytics Manager at Duke Institute for Health Innovation
Suresh Balu
Program Director at Duke School of Medicine
Cara O’Brien
Assistant Professor at Medicine at Duke University
Armando Bedoya
Associate Chief Medical Informatics Officer, Duke University Health System at Duke Health Technology Solutions
Dustin Tart
Duke University at Program Manager, Patient Response
Benjamin Goldstein
Associate Professor at Duke University
Mark Sendak
Population Health & Data Science Lead at Duke Institute for Health Innovation
William Ratliff
Innovation Program Manager at Duke University Health System

Stephanie is a third-year medical student at Duke and a 2019-2020 DIHI scholar. During her year with DIHI, she has been involved with the Detection and Treatment of Patient Deterioration project, focused on developing and integrating a machine learning model to detect adult inpatient deterioration. Prior to Duke Medical School, Stephanie was a clinical research coordinator at the Durham VA. She hopes to pursue a career in internal medicine and is also interested in identifying innovative ways to advance medical education. In her free time Stephanie enjoys trail running through the Eno State Park or training for her next road race. She is a true American and loves Dunkin’ Donuts coffee.

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I'm Harvey. I am currently an MD/PhD candidate at Duke University. I was previously a research associate at the Duke Institute for Health Innovation (DIHI), building predictive models for clinical decision support. I am interested in the intersection of medicine with computing, engineering, and innovation.

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Skilled in programming, full-stack development, SQL, predictive model building, etc. Strong information technology professional with a Master of Science - Electrical and Computer Engineering from Duke University.

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I'm a Data Enthusiast who is interested in leveraging modern computing tools and statistical/machine learning approaches to solve problems in health care, technology, and beyond. My areas of focus include statistical software, bayesian inference, machine learning, and implementation of such methods into real-world operational settings.

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Marshall is a Data Engineer at DIHI who helps design, develop, and implement modern software solutions to complex clinical problems. These solutions include everything from automated EHR data curation to deploying real-time machine learning models for clinical staff consumption and point-of-care impact. He leverages DevOps principles to ensure that these solutions are efficient to manage, deploy and enhance. Prior to joining DIHI in 2017, Marshall worked as a bioinformatician with Duke’s Center for Applied Genomics & Precision Medicine and ran Molecular Biology wetlabs investigating the human ‘omics response to infection. He completed his BS in Biochemistry at North Carolina State University and a Masters in Biology at East Carolina University. Marshall is passionate about writing elegant code that improves the lives of others. He enjoys solving incredibly difficult problems with amazing people over a great cup of Idido from Counter Culture Coffee.

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Suresh Balu serves as Associate Dean for Innovation and Partnership for the School of Medicine and as Program Director, for the Duke Institute for Health Innovation (DIHI). Suresh’s experience prior to Duke spans academia, management consulting, venture capital and private equity. As a corporate and competitive strategy consultant, Suresh worked with leading life sciences and technology firms to develop strategies for innovation, revenue generation, organizational restructuring and product portfolio planning. His PE/VC experiences include product and technology due diligence, deal structuring and portfolio company management. As an entrepreneur, Suresh was responsible for program management, market and product strategy at a venture-funded firm that focused on visualization and diagnostic products. Suresh has operational and business development experience in Asia and Europe, where he helped to establish sales channels serving government and academic clients. He is an inventor named on more than 20 US and international patents. Suresh holds MS (Computer Science) and MBA (Corporate Finance) degrees as well as a Master’s degree in Informatics and Bachelor’s degree in Engineering.

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Decisive physician with leadership and health informatics experience at Duke University Health System pursuing Health IT Master’s Degree from Duke University. Dependable, efficient, energetic, analytical and systematic with excellent interpersonal skills. Skilled in time management, organizational, and strategy skills with a passion for improving healthcare delivery.

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I am an Associate Professor of Biostatistics and Bioinformatics at Duke University with a joint appointment in the Duke Clinical Research Institute (DCRI) and a member of the Center for Predictive Medicine. I also serve as the Data Science for the Children's Health a& Discovery Initiative (CHDI) My primary research focuses on the use of Electronic Health Records for clinical research. I study how best to use EHR data for both risk prediction & assessment and comparative effectiveness. I have an interest in potential biases in EHR data and how best to design EHR based studies. I have an overall interest in the development of a "learning healthcare system" that will allow for the automatic collection of dense clinical data that can be processed and returned to clinicians and patients to allow for effective decision making. I work directly with the Duke University Health System (DUHS) to develop and evaluate clinical decision support tools. I am the analytic lead on a variety of quality improvement initiatives using health system data to improve clinical care and practice. I enjoy working with passionate health researchers asking impactful and/or complicated questions. Specialties: Statistical methods for the analysis of Electronic Health Records; prediction & machine learning methodology; comparative effectiveness studies; health policy; general biostatistics

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Mark Sendak is the Population Health & Data Science Lead at the Duke Institute for Health Innovation, where he leads interdisciplinary teams of data scientists, clinicians, and machine learning experts to build technologies that solve real clinical problems within Duke Health. He has built tools to support Duke's first adult high-utilizerprogram, chronic disease management within a Medicare Accountable Care Organization, and inpatient deterioration management. He leads a medical student innovation scholarship and is focused on building the technology infrastructure and training the workforce required to bring medicine into a new digital age. He has published in both technical venues, including Uncertainty and Artificial Intelligence and the Journal of Machine Learning Research, as well as clinical venues, including eGEMS, PlOS Medicine, and the Journal of Applied Clinical Informatics. He has worked with the National Institutes of Health (NIH) to develop a financial model for using health information technology to improve chronic kidney disease management as well as the American Medical Association (AMA) to advise on reimbursement mechanisms for artificial intelligence in healthcare. He obtained his Bachelors of Science in Mathematics from UCLA, Masters of Public Policy with a focus on health policy from Duke, and MD from Duke University School of Medicine.

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I am currently an Innovation Program Manager at The Duke Institute for Health Innovation. As part of Duke University and Duke University Health System, DIHI connects broad expertise and resources across Duke to effectively address important health care challenges and pilot innovative health care projects. Previously, I was a Consultant with Optum Advisory Services (formerly The Advisory Board). In this role, I helped lead projects focused on improving operations and reducing costs for health systems. I graduated from Duke University's Fuqua School of Business with a Health Sector Management Certificate in May of 2017. Before Fuqua, I spent 5 years in health care consulting with Cumberland Consulting Group, specializing in clinical process optimization and health IT system project design and implementation. I'm motivated by the desire to help health care providers overcome strategic and operational barriers to deliver the highest quality of care to patients.

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Stephanie Skove
Harvey Shi
Ziyuan Shen
Michael Gao
Mengxuan Cui
Marshall Nichols
Suresh Balu
Cara O’Brien
Armando Bedoya
Dustin Tart
Benjamin Goldstein
Mark Sendak
William Ratliff