Edward Wang
University of Washington
40 Papers
123 Citations
Edward Wang is an academic researcher from University of Washington. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 13, co-authored 32 publications. Previous affiliations of Edward Wang include Harvey Mudd College & University of California, San Diego.
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Papers
•Posted Content
Captum: A unified and generic model interpretability library for PyTorch.
Narine Kokhlikyan,Vivek Miglani,Miguel Martin,Edward Wang,Bilal Alsallakh,Jonathan Reynolds,Alexander Melnikov,Natalia Kliushkina,Carlos L. Araya,Siqi Yan,Orion Reblitz-Richardson +10 more
TL;DR: An interactive visualization tool called Captum Insights that is built on top of Captum library and allows sample-based model debugging and visualization using feature importance metrics and is designed for easy understanding and use.
Glabella: Continuously Sensing Blood Pressure Behavior using an Unobtrusive Wearable Device
Christian Holz,Edward Wang +1 more
- 11 Sep 2017
TL;DR: The results indicate that Glabella has the potential to serve as a socially-acceptable capture device, requiring no user input or behavior changes during regular activities, and whose continuous measurements may prove informative to physicians as well as users’ self-tracking activities.
HemaApp: noninvasive blood screening of hemoglobin using smartphone cameras
Edward Wang,William Li,Doug Hawkins,Terry Gernsheimer,Colette Norby-Slycord,Shwetak N. Patel +5 more
- 12 Sep 2016
TL;DR: HemaApp is presented, a smartphone application that noninvasively monitors blood hemoglobin concentration using the smartphone's camera and various lighting sources, and compares favorably with the control, an FDA-approved noninvasive hemoglobin measurement device.
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Seismo: Blood Pressure Monitoring using Built-in Smartphone Accelerometer and Camera
Edward Wang,Junyi Zhu,Mohit Jain,Tien-Jui Lee,Elliot Saba,Lama Nachman,Shwetak N. Patel +6 more
- 21 Apr 2018
TL;DR: This work developed and evaluated a smartphone-based BP monitoring application called textitSeismo, which relies on measuring the time between the opening of the aortic valve and the pulse later reaching a periphery arterial site.
86
Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study
Ashley E. Mason,Frederick Hecht,Shakti Davis,Joseph L. Natale,Wendy Hartogensis,Natalie Damaso,Kajal T. Claypool,Stephan Dilchert,Subhasish Dasgupta,Shweta Purawat,Varun Viswanath,Amit Klein,Anoushka Chowdhary,Sarah M. Fisher,Claudine Anglo,Karena Puldon,Danou Veasna,Jenifer G. Prather,Leena S. Pandya,Lindsey M Fox,M R Busch,Casey Giordano,Brittany K. Mercado,Jining Song,Rafael Gómez Jaimes,Brian Baum,Brian A. Telfer,Casandra Philipson,Paula P. Collins,Adam Rao,Edward Wang,Rachel H. Bandi,Bianca Judy Choe,Elissa S. Epel,Stephen K. Epstein,Joanne B. Krasnoff,Marco B. Lee,Shih-Wen Lee,Gina Lopez,Arpan Mehta,Laura Melville,Tiffany S. Moon,Lilianne R. Mujica-Parodi,Kimberly Noel,Michael A. Orosco,Jesse M. Rideout,Janet D. Robishaw,Robert M. Rodriguez,Kaushal Shah,Jonathan H. Siegal,Amarnath Gupta,Ilkay Altintas,Benjamin L. Smarr +52 more
TL;DR: In this article , the authors used wearable devices to continuously measure physiological metrics for early illness detection, including temperature, to identify the onset of COVID-19 using machine learning classification, achieving an overall ROC of 0.819 (95% CI [0.809, 0.830]).