F
Florencia Garcia Vicente
Researcher at Northwestern University
Publications - 4
Citations - 42
Florencia Garcia Vicente is an academic researcher from Northwestern University. The author has contributed to research in topics: Upload. The author has an hindex of 1, co-authored 4 publications receiving 8 citations.
Papers
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Journal ArticleDOI
Non-Invasive Wearable Patch Utilizing Seismocardiography for Peri-Operative Use in Surgical Patients
Beren Semiz,Andrew M. Carek,Jessica C. Johnson,Shireen Ahmad,J. Alex Heller,Florencia Garcia Vicente,Stacey Caron,Charles W. Hogue,Mozziyar Etemadi,Omer T. Inan +9 more
TL;DR: A wearable patch mounted on the mid-sternum is proposed, which captures the seismocardiogram and electrocardiogram signals continuously to predict SV in patients undergoing major surgery, showing promise for the proposed wearable-based methodology to be used as an alternative to TED for continuous patient monitoring and guiding peri-operative fluid management.
Posted Content
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases.
Zaid Nabulsi,Andrew Sellergren,Shahar Jamshy,Charles Lau,Eddie Santos,Atilla Peter Kiraly,Wenxing Ye,Jie Yang,Sahar Kazemzadeh,Jin Yu,Raju Kalidindi,Mozziyar Etemadi,Florencia Garcia Vicente,David S. Melnick,Greg S. Corrado,Lily Peng,Krish Eswaran,Daniel Tse,Neeral Beladia,Yun Liu,Po-Hsuan Cameron Chen,Shravya Shetty +21 more
TL;DR: An AI system to classify CXRs as normal or abnormal and the results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases.
Journal ArticleDOI
Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI.
Ethan M. I. Johnson,J. Alex Heller,Florencia Garcia Vicente,Roberto Sarnari,Daniel Z. Gordon,Patrick M. McCarthy,Alex J. Barker,Mozziyar Etemadi,Michael Markl +8 more
TL;DR: This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects that may support development of an easy clinical test used to identify potential aortic flow abnormalities.
Journal ArticleDOI
Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning.
Rajesh N. Keswani,Daniel Byrd,Florencia Garcia Vicente,J. Alex Heller,Matthew William Klug,Nikhilesh R. Mazumder,Jordan Wood,Anthony D. Yang,Mozziyar Etemadi +8 more
TL;DR: In this paper, the authors presented a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos by matching recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extracting frames of interest.