Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

16 August 2023

Papers citing "Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities"

7 / 7 papers shown

Title
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes? Byung-Hak Kim Zhongfen Deng Philip S. Yu Varun Ganapathi ELM 30 6 0 28 Oct 2022
Improving ECG Classification Interpretability using Saliency Maps Yola Jones F. Deligianni Jeffrey Stephen Dalton FAtt 18 19 0 10 Jan 2022
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 76 70 0 02 Mar 2021
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 120 293 0 17 Oct 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,143 0 23 Aug 2019
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 219 201 0 06 Jul 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,231 0 24 Jun 2017