Search Results for author: Narges Razavian

Found 17 papers, 12 papers with code

Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning

1 code implementation23 Mar 2022 Weicheng Zhu, Carlos Fernandez-Granda, Narges Razavian

The resulting representations and clusters from self-supervision are used as features of a survival model for recurrence prediction at the patient level.

Multiple Instance Learning Self-Supervised Learning +1

Deep Probability Estimation

no code implementations21 Nov 2021 Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda

Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.

Autonomous Vehicles Binary Classification +1

Causal Effect Variational Autoencoder with Uniform Treatment

no code implementations16 Nov 2021 Daniel Jiwoong Im, Kyunghyun Cho, Narges Razavian

In this paper, we introduce uniform treatment variational autoencoders (UTVAE) that are trained with uniform treatment distribution using importance sampling and show that using uniform treatment over observational treatment distribution leads to better causal inference by mitigating the distribution shift that occurs from training to test time.

Causal Inference Domain Adaptation

Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning

1 code implementation NeurIPS 2021 Aakash Kaku, Sahana Upadhya, Narges Razavian

Our analysis reveals that models trained via our approach have higher feature reuse compared to a standard MoCo and learn informative features earlier in the network.

Self-Supervised Learning

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

1 code implementation4 Aug 2020 Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

COVID-19 Diagnosis Decision Making +1

Early-Learning Regularization Prevents Memorization of Noisy Labels

2 code implementations NeurIPS 2020 Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

In contrast with existing approaches, which use the model output during early learning to detect the examples with clean labels, and either ignore or attempt to correct the false labels, we take a different route and instead capitalize on early learning via regularization.

General Classification Learning with noisy labels +1

BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining

no code implementations EMNLP (ClinicalNLP) 2020 Zachariah Zhang, Jingshu Liu, Narges Razavian

In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding from EHR notes, utilizing recently developed unsupervised pretraining that have achieved state of the art performance on a variety of NLP tasks.

Variationally Regularized Graph-based Representation Learning for Electronic Health Records

1 code implementation8 Dec 2019 Weicheng Zhu, Narges Razavian

A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.

Graph structure learning Representation Learning

Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction

no code implementations18 Nov 2019 Josie Williams, Narges Razavian

We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models.

BIG-bench Machine Learning

DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

1 code implementation13 Nov 2019 Aakash Kaku, Chaitra V. Hegde, Jeffrey Huang, Sohae Chung, Xiuyuan Wang, Matthew Young, Alireza Radmanesh, Yvonne W. Lui, Narges Razavian

This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model.

Brain Segmentation

Deep EHR: Chronic Disease Prediction Using Medical Notes

1 code implementation Machine Learning for Health Care conference 2018 Jingshu Liu, Zachariah Zhang, Narges Razavian

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation.

Disease Prediction Management

Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests

1 code implementation2 Aug 2016 Narges Razavian, Jake Marcus, David Sontag

Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task.

Temporal Convolutional Neural Networks for Diagnosis from Lab Tests

1 code implementation25 Nov 2015 Narges Razavian, David Sontag

Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends.


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