Fetching the paper…
Reading the bibliography…
The development of neural networks for clinical artificial intelligence (AI) is reliant on interpretability, transparency, and performance.
Publicly available clinical bert embeddings
Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew B. A. McDermott · 1904
Earlier work this paper cites.
Clinicalbert: Modeling clinical notes and predicting hospital readmission
Kexin Huang, Jaan Altosaar, and Rajesh Ranganath · 1904
Earlier work this paper cites.
What does bert look at? an analysis of bert’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 1906
Earlier work this paper cites.
Med7: a transferable clinical natural language processing model for electronic health records
Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, and Alejo Nevado-Holgado · 2003
Earlier work this paper cites.
Learning from the best: Rationalizing prediction by adversarial information calibration
Lei Sha, Oana-Maria Camburu, and Thomas Lukasiewicz · 2012
Earlier work this paper cites.
Lung cancer classification using neural networks for ct images
Jinsa Kuruvilla and K. Gunavathi · 2013
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus Robert Müller, and Wojciech Samek · 2015
Earlier work this paper cites.
Explaining predictions of non-linear classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2016
Earlier work this paper cites.
Predicting suicidal behavior from longitudinal electronic health records
Yuval Barak-Corren, Victor M. Castro, Solomon Javitt, Alison G. Hoffnagle, Yael Dai, Roy H. Perlis, Matthew K. Nock, Jordan W. Smoller, and Ben Y. Reis · 2016
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li Wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark · 2016
Earlier work this paper cites.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
Cited alongside, same era.
Artificial intelligence in healthcare: Past, present and future
Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang · 2017
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus Robert Müller · 2017
Cited alongside, same era.
Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach
Wei Hung Weng, Kavishwar B. Wagholikar, Alexa T. McCray, Peter Szolovits, and Henry C. Chueh · 2017
Cited alongside, same era.
Explainable prediction of medical codes from clinical text
James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein · 2018
Cited alongside, same era.
Inferring which medical treatments work from reports of clinical trials
Eric Lehman, Jay DeYoung, Regina Barzilay, and Byron Wallace · 2019
Later among the works it cites.
Layer-Wise Relevance Propagation: An Overview , pages 193–209
Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller · 2019
Later among the works it cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
Later among the works it cites.
Benchmarking deep learning architectures for predicting readmission to the icu and describing patients-at-risk
Sebastiano Barbieri, James Kemp, Oscar Perez-Concha, Sradha Kotwal, Martin Gallagher, Angus Ritchie, and Louisa Jorm · 2020
Later among the works it cites.
Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A comparison of word embeddings for the biomedical natural language processing
Yanshan Wang, Sijia Liu, Naveed Afzal, Majid Rastegar-Mojarad, Liwei Wang, Feichen Shen, Paul Kingsbury, and Hongfang Liu · 2018
Cited alongside, same era.
Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
Cited alongside, same era.
Later among the works it cites.
Behrt: transformer for electronic health records
Yikuan Li, Shishir Rao, Jose Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi · 2020
Later among the works it cites.
What’s in a note? unpacking predictive value in clinical note representations, 2020
Tristan Naumann and William Boag · 2020
Later among the works it cites.
Natural language processing for structuring clinical text data on depression using uk-cris
Nemanja Vaci, Qiang Liu, Andrey Kormilitzin, Franco De Crescenzo, Ayse Kurtulmus, Jade Harvey, Bessie O’Dell, Simeon Innocent, Anneka Tomlinson, Andrea Cipriani, et al · 2020
Later among the works it cites.
Does explainable artificial intelligence improve human decision-making?
Yasmeen Alufaisan, Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou, and Murat Kantarcioglu · 2021
Closest in time.