J
Jessica K De Freitas
Researcher at Icahn School of Medicine at Mount Sinai
Publications - 33
Citations - 1899
Jessica K De Freitas is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Deep learning & Coronary artery disease. The author has an hindex of 13, co-authored 31 publications receiving 980 citations. Previous affiliations of Jessica K De Freitas include Mount Sinai Hospital & National Institutes of Health.
Papers
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Proceedings ArticleDOI
Evaluation of patient re-identification using laboratory test orders and mitigation via latent space variables.
TL;DR: It is demonstrated that releasing latentspace encoded laboratory orders significantly improves privacy compared to releasing raw laboratory orders (<5% re-identification), while preserving information contained within the laboratory orders (AUC of >0.9 for recreating encoded values).
Book ChapterDOI
Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions
Tingyi Wanyan,Martin Kang,Marcus A. Badgeley,Kipp W. Johnson,Jessica K De Freitas,Fayzan Chaudhry,Akhil Vaid,Shan Zhao,Riccardo Miotto,Girish N. Nadkarni,Fei Wang,Justin F. Rousseau,Ariful Azad,Ying Ding,Benjamin S. Glicksberg +14 more
TL;DR: This work proposes a relational, deep heterogeneous network learning method that operates on EHR data and shows that relational graph learning naturally encodes structured relationships in the EHR and outperforms traditional feed forward models in the prediction of thousands of diseases.
Posted Content
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients.
Tingyi Wanyan,Hossein Honarvar,Suraj K. Jaladanki,Chengxi Zang,Nidhi Naik,Sulaiman Somani,Jessica K De Freitas,Ishan Paranjpe,Akhil Vaid,Riccardo Miotto,Girish N. Nadkarni,Marinka Zitnik,ArifulAzad,Fei Wang,Ying Ding,Benjamin S. Glicksberg +15 more
TL;DR: In this paper, contrastive loss (CL) was used to improve the performance of cross-entropy loss (CEL) for imbalanced EHR data and the related COVID-19 analyses.
Journal ArticleDOI
Exploring the Potential for Collaborative Use of an App-Based Platform for n-of-1 Trials Among Healthcare Professionals That Treat Patients With Insomnia.
TL;DR: The potential for implementing a mobile app-based n-of-1 trial platform for collaborative use by clinicians and patients to support data-driven decisions around the treatment of insomnia is explored.
Book ChapterDOI
Deep learning for biomedical applications
TL;DR: In this article, the authors discuss the challenges to implement and deploy augmented human intelligence based on deep learning in the clinical domain, and discuss the limitations and needs for improved methods and applications.