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Deployment of a Robust and Explainable Mortality Prediction Model: The
  COVID-19 Pandemic and Beyond

Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond

28 November 2023
Jacob R. Epifano
Stephen Glass
Ravichandran Ramachandran
Sharad Patel
A. Masino
Ghulam Rasool
ArXiv (abs)PDFHTML

Papers citing "Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond"

12 / 12 papers shown
Title
Drug discovery with explainable artificial intelligence
Drug discovery with explainable artificial intelligence
José Jiménez-Luna
F. Grisoni
G. Schneider
181
636
0
01 Jul 2020
Influence Functions in Deep Learning Are Fragile
Influence Functions in Deep Learning Are Fragile
S. Basu
Phillip E. Pope
Soheil Feizi
TDI
125
235
0
25 Jun 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation
  Methods
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Dylan Slack
Sophie Hilgard
Emily Jia
Sameer Singh
Himabindu Lakkaraju
FAttAAMLMLAU
77
819
0
06 Nov 2019
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction
  to Concepts and Methods
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier
Willem Waegeman
PERUD
244
1,421
0
21 Oct 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for
  Clinical End Use
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
S. Tonekaboni
Shalmali Joshi
M. Mccradden
Anna Goldenberg
73
396
0
13 May 2019
Rethinking clinical prediction: Why machine learning must consider year
  of care and feature aggregation
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Bret A. Nestor
Matthew B. A. McDermott
Geeticka Chauhan
Tristan Naumann
Michael C. Hughes
Anna Goldenberg
Marzyeh Ghassemi
OOD
43
35
0
30 Nov 2018
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
21,939
0
22 May 2017
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Percy Liang
TDI
213
2,899
0
14 Mar 2017
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced
  Datasets in Machine Learning
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
G. Lemaître
Fernando Nogueira
Christos K. Aridas
79
2,066
0
21 Sep 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
16,990
0
16 Feb 2016
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
169
3,275
0
05 Dec 2014
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh Chawla
Kevin W. Bowyer
Lawrence Hall
W. Kegelmeyer
AI4TS
373
25,678
0
09 Jun 2011
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