Explaining an increase in predicted risk for clinical alerts

10 July 2019

Papers citing "Explaining an increase in predicted risk for clinical alerts"

28 / 28 papers shown

Title
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 70 394 0 13 May 2019
Dice in the Black Box: User Experiences with an Inscrutable Algorithm Aaron Springer Victoria Hollis S. Whittaker 36 38 0 07 Dec 2018
Did the Model Understand the Question? Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan Kedar Dhamdhere ELM OOD FAtt 53 197 0 14 May 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 131 574 0 21 Feb 2018
Scalable and accurate deep learning for electronic health records A. Rajkomar Eyal Oren Kai Chen Andrew M. Dai Nissan Hajaj ... A. Butte M. Howell Claire Cui Greg S. Corrado Jeffrey Dean OOD BDL 168 2,144 0 24 Jan 2018
Improving Palliative Care with Deep Learning Anand Avati Kenneth Jung S. Harman L. Downing A. Ng N. Shah 136 371 0 17 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 98 685 0 02 Nov 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 201 2,221 0 12 Jun 2017
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis B. Shickel P. Tighe A. Bihorac Parisa Rashidi BDL 86 1,116 0 12 Jun 2017
Clinical Intervention Prediction and Understanding using Deep Networks Harini Suresh Nathan Hunt Alistair E. W. Johnson Leo Anthony Celi Peter Szolovits Marzyeh Ghassemi OOD 54 132 0 23 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,986 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 399 3,787 0 28 Feb 2017
Supervised topic models for clinical interpretability M. C. Hughes Huseyin Melih Elibol T. McCoy R. Perlis Finale Doshi-Velez 53 9 0 06 Dec 2016
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Edward Choi M. T. Bahadori Joshua A. Kulas A. Schuetz Walter F. Stewart Jimeng Sun AI4TS 115 1,245 0 19 Aug 2016
Deepr: A Convolutional Net for Medical Records Phuoc Nguyen T. Tran N. Wickramasinghe Svetha Venkatesh MedIm 55 373 0 26 Jul 2016
Modeling Missing Data in Clinical Time Series with RNNs Zachary Chase Lipton David C. Kale R. Wetzel AI4TS 57 229 0 13 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,699 0 10 Jun 2016
Recurrent Neural Networks for Multivariate Time Series with Missing Values Zhengping Che S. Purushotham Kyunghyun Cho David Sontag Yan Liu AI4TS 308 1,936 0 06 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Y. Gal Zoubin Ghahramani UQCV DRL BDL 180 1,650 0 16 Dec 2015
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain Zhengping Che S. Purushotham R. Khemani Yan Liu 47 139 0 11 Dec 2015
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks Edward Choi M. T. Bahadori A. Schuetz Walter F. Stewart Jimeng Sun 143 1,100 0 18 Nov 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,667 0 21 Dec 2014
Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio AIMat 552 27,300 0 01 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,292 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,876 0 12 Nov 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 130 1,103 0 06 Dec 2009