Narges Razavian
Narges Razavian
New York University Medical Center
Verified email at - Homepage
Cited by
Cited by
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
N Coudray, PS Ocampo, T Sakellaropoulos, N Narula, M Snuderl, ...
Nature medicine 24 (10), 1559-1567, 2018
Association of psychiatric disorders with mortality among patients with COVID-19
K Nemani, C Li, M Olfson, EM Blessing, N Razavian, J Chen, E Petkova, ...
JAMA psychiatry 78 (4), 380-386, 2021
Early-learning regularization prevents memorization of noisy labels
S Liu, J Niles-Weed, N Razavian, C Fernandez-Granda
arXiv preprint arXiv:2007.00151, 2020
Population-level prediction of type 2 diabetes from claims data and analysis of risk factors
N Razavian, S Blecker, AM Schmidt, A Smith-McLallen, S Nigam, ...
Big Data 3 (4), 277-287, 2015
Multi-task prediction of disease onsets from longitudinal laboratory tests
N Razavian, J Marcus, D Sontag
Machine learning for healthcare conference, 73-100, 2016
Deep ehr: Chronic disease prediction using medical notes
J Liu, Z Zhang, N Razavian
Machine Learning for Healthcare Conference, 440-464, 2018
State of the art: machine learning applications in glioma imaging
E Lotan, R Jain, N Razavian, GM Fatterpekar, YW Lui
American Journal of Roentgenology 212 (1), 26-37, 2019
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzębski, ...
NPJ digital medicine 4 (1), 80, 2021
Temporal convolutional neural networks for diagnosis from lab tests
N Razavian, D Sontag
arXiv preprint arXiv:1511.07938, 2015
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients
N Razavian, VJ Major, M Sudarshan, J Burk-Rafel, P Stella, H Randhawa, ...
NPJ digital medicine 3 (1), 130, 2020
Predicting childhood obesity using electronic health records and publicly available data
R Hammond, R Athanasiadou, S Curado, Y Aphinyanaphongs, C Abrams, ...
PloS one 14 (4), e0215571, 2019
On the design of convolutional neural networks for automatic detection of Alzheimer’s disease
S Liu, C Yadav, C Fernandez-Granda, N Razavian
Machine Learning for Health Workshop, 184-201, 2020
BERT-XML: Large scale automated ICD coding using BERT pretraining
Z Zhang, J Liu, N Razavian
arXiv preprint arXiv:2006.03685, 2020
Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models
R Hong, W Liu, D DeLair, N Razavian, D Fenyö
Cell Reports Medicine 2 (9), 100400, 2021
A deep learning approach for rapid mutational screening in melanoma
RH Kim, S Nomikou, N Coudray, G Jour, Z Dawood, R Hong, E Esteva, ...
BioRxiv, 610311, 2019
Patient condition identification and treatment
NS Razavian, S Blecker, AM Schmidt, A Smith-McLallen, S Nigam, ...
US Patent App. 15/494,354, 2017
Artificial intelligence and cancer
O Troyanskaya, Z Trajanoski, A Carpenter, S Thrun, N Razavian, N Oliver
Nature cancer 1 (2), 149-152, 2020
Document representation and quality of text: An analysis
M Keikha, NS Razavian, F Oroumchian, HS Razi
Survey of text mining II: Clustering, classification, and retrieval, 219-232, 2008
DARTS: DenseUnet-based automatic rapid tool for brain segmentation
A Kaku, CV Hegde, J Huang, S Chung, X Wang, M Young, A Radmanesh, ...
arXiv preprint arXiv:1911.05567, 2019
An overview of nonparametric bayesian models and applications to natural language processing
N Sharif-Razavian, A Zollmann
Science, 71-93, 2008
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