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Summit: Machine Learning [clear filter]
Thursday, April 11

2:55pm EDT

MRI Image Synthesis for the Diagnosis of Parkinson's Disease
Parkinsons disease (PD) is a neurodegenerative disease that affects an estimated 1% of adults over 65. While the disease itself is not fatal, complications related to PD are rated as the 14th largest cause of death in the United States by the Center of Disease Control and Prevention. In spite of technological advances, Parkinson’s diagnosis methods have not changed, and the accuracy of diagnosis has remained at approximately 81% for the past 25 years. These methods include analyzing years of neurological data to determine if the patient has developed the symptoms of Parkinson’s, including limb rigidity and tremors, both of which are common side effects of a number of other diseases. With the rise of automated prediction algorithms paired with the generation of massive amounts of data, the automatic diagnosis of Parkinson’s Disease has not caught up to traditional means. This is commonly attributed to the lack of useful data, as most computational systems require a tremendous amount of medical data that isn’t readily available as gathering the data can be expensive. This study presents PDGAN, a tool to aid pathologists and neurologists in the diagnosis of Parkinson’s Disease. PDGAN uses a series of neural networks to classify Magnetic Resonance Images (MRI Images). PDGAN employs Generative Adversarial Networks (GANs) to synthetically generate medical images which is used to augment the classification efforts. The pair of Convolutional Neural Networks exhibited an testing accuracy of 91.4% without the augment of new data, and combined the total accuracy was 96.6%, a 16% increase com- pared to traditional methods at a fraction of the cost and time. PDGAN demonstrates the feasibility of utilizing GANs to generate unseen data for the improvement of classification accuracy in the medical setting. The biggest challenge in leveraging the power of AI/ML in the healthcare space today is a lack of data access. What if we could artificially generate more data to fill in the gaps where traditional patient-driven sampling techniques don’t meet the data demand? This session presents one use-case for Generative Adversarial Networks and Auto-encoders to augment a data-set, as well as other optimization techniques for classifiers working with small amounts of data. This session will demonstrate these concepts in the scope of an important problem in the field of healthcare: accurately assessing Parkinson’s onset for a quick diagnosis. 

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avatar for Neeyanth Kopparapu

Neeyanth Kopparapu

Student, Thomas Jefferson High School for Science and Technology
Neeyanth Kopparapu is a High School Junior at Thomas Jefferson High School in Alexandria, VA. As a student, he is interested in mathematics, computer science, and physics. He is especially interested in developing computer science (specifically artificial intelligence) models to help... Read More →


Thursday April 11, 2019 2:55pm - 3:25pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA

3:25pm EDT

Modeling and Forecasting Hospital Workforce Supply to Improve Outcomes
I will present initial findings from a project involving a large academic medical center looking to develop a predictive system that optimizes nurse staffing. The hospital is looking to reduce costs and improve patient care by staffing the right number of nurses, with the right skills and experience, on every shift, across every unit. I will discuss how we are integrating nurse workforce data to understand labor supply variations and compare this to patient demand — including volume predictions as well as patient condition metrics. Using forecasting models, optimization, and machine learning we are building a system that will recommend optimal staffing levels for each unit so that nurse managers can staff more accurately, reducing over- or under-staffing for a given period. 

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avatar for John Harris

John Harris

Data Scientist, Cogit Analytics
John is the chief data scientist for Cogit Analytics. He has extensive experience designing and building advanced analytics, machine learning, and other quantitative solutions in supply chain, finance, and healthcare.


Thursday April 11, 2019 3:25pm - 3:40pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA

3:50pm EDT

Machine Learning as a Tool to Address HIV Health Disparities
HIV infection is a world-wide epidemic with almost 20 million people infected and tens of millions of deaths since the early 1980’s.  Due to better healthcare access and treatment, HIV morbidity and mortality in the US isn’t as dire for some subpopulations and has declined ~ 19% between 2005-2016. Unfortunately, there are specific demographic groups in the US which carry the burden of the disease far more than others. African Americans, especially those living in the South, lag behind other regions of the US in key HIV prevention and care indicators resulting in disparities in HIV incidence and prevalence. With the knowledge these disparities are not adequately accounted for through high-risk sexual behavior like the frequency of unprotected intercourse or the number of sexual partners, new tools are desperately needed to assist in addressing the disparities in HIV incidence /prevalence in minority communities.
This talk proposes to cover the following: 1) Highlight recent applications of machine learning in studies on HIV, 2) Discuss the downsides of ML healthcare, and 3) Present the results of a novel HIV health disparities study, applying machine learning/logistic regression techniques.

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avatar for Kimberly Deas

Kimberly Deas

Health Data Scientist, Rutgers University
Kimberly is a Health Data Scientist Consultant who works with for and non-profit organizations, academic institutions, and government agencies, providing statistical and data analytical support to their programs. Formerly educated in Chemistry and Pharmacology at the University of... Read More →


Thursday April 11, 2019 3:50pm - 4:20pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA

4:25pm EDT

Challenges in Clinical Big Data Research: A Case Discussion
A discussion of the UVA Engineering in Medicine Partnership on Sleep and Cardiovascular Research group’s experience with conducting research on a clinically- derived big data set, specifically focusing on the challenges this group has faced, and how those challenges are generalizable to all clinical big data research.

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avatar for Elizabeth Harrison

Elizabeth Harrison

Dual Degree Candidate, University of Virginia School of Medicine & School of Data Science
Elizabeth Harrison is a fourth year medical student at the University of Virginia School of Medicine and a recent graduate of UVA's Master of Science in Data Science degree program. She is currently working on a number of clinical research projects involving the application of data... Read More →


Thursday April 11, 2019 4:25pm - 4:40pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA

4:45pm EDT

Ontology and Oncology: NLP for Precision Medicine
This session gives an overview of the importance of precision medicine in cancer treatment and describes an approach used by UVA in the TREC 2018 Precision Medicine workshop. The PM track aims to encourage research into precision oncology medicine to provide more relevant information to physicians and researchers.

For this task we ranked articles from a corpus of bio-medical article abstracts from PubMed and MEDLINE for relevance for the treatment, prevention, and prognosis of the disease given specific medical information about each patient.

We demonstrated using a flexible graph-based query expansion method that existing medical ontologies can be leveraged to improve precision in document relevance ranking with little to no other clinical input.

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avatar for Sean Mullane

Sean Mullane

Data Scientist, UVA Health System
Sean is currently working as a data scientist in the UVA Health System while completing a Master's degree at the UVA Data Science Institute, where he is researching the applications of machine learning to protein structure prediction. He has lived in Charlottesville, VA since graduating... Read More →


Thursday April 11, 2019 4:45pm - 5:00pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA

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