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C7 Semantic Nutrition: A Word2Vec Process to Estimate Nutrition with Mobile Assistants

Joshua D’Arcy
Machine Learning Engineer at Atriomx Health
+ 3 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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C7 Semantic Nutrition: A Word2Vec Process to Estimate Nutrition with Mobile Assistants
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About speakers

Joshua D’Arcy
Machine Learning Engineer at Atriomx Health
Sabrina Qi
Edison Engineer at GE Healthcare
Dori Steinberg
Director Of Research at Equip
Jessilyn Dunn
Assistant Professor at Duke University

During my time in medical school, I developed a passion for proactive healthcare. Recent advances in data mining/data liquidation, wearable devices, machine learning, and behavior science offer new and exciting opportunities to alter the health trajectory of patients-- often before they have any disease burden at all. Each of my research projects revolve around a central thesis: We already have the core technologies and data to prevent people from getting sick, and the scientific community has a shared responsibility to implement effective, validated models to improve lives. Outside of research, I'm an avid podcastee (Snacks Daily for life), home technology enthusiast, and software hobbyist. My most time-consuming hobby is collecting objective metrics about my life through software and forecasting models to alter my own behavior (there are at least dozens of us who do this). I also love making terrible puns, and starting new books that I know I'll never finish.

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I am an recent graduate from Duke University, where I studied Biomedical Engineering and Global Health. Currently, I am an Edison Engineer at GE Healthcare, working with the Digital business in a software engineering role. Outside of work, I enjoy dancing, reading, and volunteering in the community.

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Dr. Steinberg is an Associate Professor in the Duke School of Nursing and at the Duke Global Health Institute. She is also Director of the Duke Global Digital Health Science Center. Her research focuses on digital health interventions for dietary change, and chronic disease management among adults. Dr. Steinberg is the PI of an NIH-funded R01 grant examining how to best leverage digital health to improve diet quality among individuals with high blood pressure. She was PI on K12 career development grant as Duke BIRCWH Scholar and has been a co-investigator on several successfully funded grants from NIH and Duke. Her work has been featured in the Journal of the American Medical Association, The Journal of the Academy of Nutrition and Dietetics and The American Journal of Public Health, as well as in mass media. Dr. Steinberg earned her B.S. in Nutrition from the Cornell University, her M.S. in Public Health from Harvard University, and a Ph.D. in Nutrition from the University of North Carolina at Chapel Hill. She is also a Registered Dietitian.

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Developing new tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease. Data sources include digital health/mobile health/wearable devices, multi-omics, and electronic health records.

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Hi, my name is Joshua D'Arcy. I'm going to talk to you about a process that we developed to Duke University that we tallest magic magician nutrition is the idea that we can treat nutrition and as a result, Is a process that tracks in outpatient participants dietary intake. It's usually done in the form of written diary which requires a significant investment by the patient and physician who reads and transcribe it. It's a big ask for patient compliance and an even bigger ass for healthcare

stuff. Listen to this problem with you didn't have the data using Mobile assistance, roughly 80% of people and the United States on a smartphone with access to a Mobile Assistant Weebly transcription, using these mobile system lowered the barrier to date. Same thing and similar names for even a radically different things without this made for a really interesting problem and so our solution was to implement a word to back bottle that smes nutrition. Our database based on the closest semantic neighbor of which we know about you,

Here's how it works. You can simply say, hey, Mobile Assistant. I'm eating a serving of apple cobbler within earshot of listening device. I have personally never heard of apple cobbler before. So are online for the nearest beach. Make this Food Lion as simple as OK Google, I'm eating a caesar salad. Nutrition bar. You can find more thorough demonstrations, that are public GitHub repo, including instructions on how to build your own database and connect your

favor Mobile Assistant. And please take a chance to visit that repo. I we would love your feedback as we stand on the process to make it available for everyone to use. So please feel free to tweet us at Big Ideas and thank you so much for your time.

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Joshua D’Arcy
Sabrina Qi
Dori Steinberg
Jessilyn Dunn