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Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care.
Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 1912
Earlier work this paper cites.
Cost-effectiveness of magnetic resonance imaging with a new contrast agent for the early diagnosis of alzheimer’s disease
Maria Biasutti, Natacha Dufour, Clotilde Ferroud, William Dab, and Laura Temime · 1932
Earlier work this paper cites.
Systematic analysis of breast cancer morphology uncovers stromal features associated with survival
AH Beck, AR Sangoi, S Leung, RJ Marinelli, TO Nielsen, MJ van de Vijver, RB West, M van de Rijn, and D Koller · 1946
Earlier work this paper cites.
Classification and regression trees
Leo Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone · 1984
Earlier work this paper cites.
Decision making in dentistry. part i: A historical and methodological overview
Ann M. McCreery and Edmond Truelove · 1991
Earlier work this paper cites.
An input output hmm architecture
Yoshua Bengio and Paolo Frasconi · 1995
Earlier work this paper cites.
A framework for behavioural cloning
Michael Bain and Claude Sammut · 1996
Earlier work this paper cites.
Efficacy of venlafaxine and placebo during long-term treatment of depression: A pooled analysis of relapse rates
A. R. Entsuah, R. L. Rudolph, D. Hackett, and S. Miska · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Rates and risk factors for dementia and alzheimer’s disease: results from eurodem pooled analyses. eurodem incidence research group and work groups. european studies of dementia
LJ Launer, K Andersen, ME Dewey, L Letenneur, A Ott, LA Amaducci, C Brayne, JR Copeland, JF Dartigues, P Kragh-Sorensen, Lobo A, Martinez-Lage JM, T Stijnen, and A Hofman · 1999
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
Earlier work this paper cites.
Risk factors for alzheimer’s disease: a prospective analysis from the canadian study of health and aging
J Lindsay, D Laurin, R Verreault, R Hébert, B Helliwell, GB Hill, and I McDowell · 2002
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Learning ”what-if” explanations for sequential decision-making
Ioana Bica, Daniel Jarrett, Alihan Hüyük, and Mihaela van der Schaar · 2007
Earlier work this paper cites.
Diagnosis and treatment of low back pain: A joint clinical practice guideline from the american college of physicians and the american pain society
Roger Chou, Amir Qaseem, Vincenza Snow, Donald Casey, Thomas J. Cross, Paul Shekelle, and Douglas K. Owens · 2007
Earlier work this paper cites.
Sources of variation in physician adherence with clinical guidelines: Results from a factorial experiment
J. B. McKinlay, C. L. Link, K. M. Freund, L. D. Marceau, A. B. O’Donnell, and K. L. Lutfey · 2007
Earlier work this paper cites.
Guidelines for the management of hospital-acquired pneumonia in the uk: Report of the working party on hospital-acquired pneumonia of the british society for antimicrobial chemotherapy
R. G. Masterton, A. Galloway, G. French, M. Street, J. Armstrong, E. Brown, J. Cleverley, P. Dilworth, C. Fry, A. D. Gascoigne, Alan Knox, Dilip Nathwani, Robert Spencer, and Mark Wilcox · 2008
Earlier work this paper cites.
Staging dementia using clinical dementia rating scale sum of boxes scores: a texas alzheimer’s research consortium study
SE O’Bryant, SC Waring, CM Cullum, J Hall, L Lacritz, PJ Massman, PJ Lupo, JS Reisch, and R Doody · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew Maas, J Andrew Bagnell, and Anind K Dey · 2008
Earlier work this paper cites.
Inverse reinforcement learning in partially observable environments
Jaedeug Choi and Kee-Eung Kim · 2011
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Soft decision trees
Ozan Irsoy, Olcay Taner Yıldız, and Ethem Alpaydın · 2012
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Apprenticeship learning for model parameters of partially observable environments
Takaki Makino and Johane Takeuchi · 2012
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Probabilistic model-based imitation learning
Peter Englert, Alexandros Paraschos, Marc Peter Deisenroth, and Jan Peters · 2013
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Boosted and reward-regularized classification for apprenticeship learning
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Serial, parallel and hierarchical decision making in primates
Ariel Zylberberg, Jeannette AM Lorteije, Brian G Ouellette, Chris I De Zeeuw, Mariano Sigman, and Pieter Roelfsema · 2017
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A bayesian approach to generative adversarial imitation learning
Wonseok Jeon, Seokin Seo, and Kee-Eung Kim · 2018
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Human-in-the-loop interpretability prior
Isaac Lage, Andrew Slavin Ross, Been Kim Google Brain, Samuel J Gershman, and Finale Doshi-Velez · 2018
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Dementia: Assessment, management and support for people living with dementia and their carers
NICE · 2018
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, and Jan Peters · 2018
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Cited alongside, same era.
A sequential decision-theoretic model for medical diagnostic system
A Li, S Jin, L Zhang, and Y Jia · 2015
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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
Cited alongside, same era.
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
V Gulshan, L Peng, M Coram, MC Stumpe, D Wu, A Narayanaswamy, S Venugopalan, K Widner, T Madams, J Cuadros, R Kim, R Raman, PC Nelson, JL Mega, and DR Webster · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun · 2017
Cited alongside, same era.
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, and Swarat Chaudhuri · 2018
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Medical practice variation: Public reporting a first necessary step to spark change
Gert P. Westert, Stef Groenewoud, John E. Wennberg, Catherine Gerard, Phil Dasilva, Femke Atsma, and David C. Goodman · 2018
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Adaptive neural trees
Ryutaro Tanno, Kai Arulkumaran, Daniel C Alexander, Antonio Criminisi, and Aditya Nori · 2019
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Interpretable policy learning
Alex James Chan · 2020
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Scalable bayesian inverse reinforcement learning
Alex James Chan and Mihaela van der Schaar · 2020
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Early prediction of circulatory failure in the intensive care unit using machine learning
Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, and Tobias M. Merz · 2020
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Inverse active sensing: Modeling and understanding timely decision-making
Daniel Jarrett and Mihaela van der Schaar · 2020
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Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in france
M.-C Laï, M Brian, and M.-F Mamzer · 2020
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Optimization methods for interpretable differentiable decision trees in reinforcement learning
Andrew Silva, Taylor Killian, Ivan Rodriguez Jimenez, Sung-Hyun Son, and Matthew Gombolay · 2020
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What did you think would happen? explaining agent behaviour through intended outcomes
Herman Yau, Chris Russell, and Simon Hadfield · 2020
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Cdt: Cascading decision trees for explainable reinforcement learning
Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding, Changjian Li, and Ruitong Huang · 2021
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Explaining by imitating: Understanding decisions by interpretable policy learning
Alihan Hüyük, Daniel Jarrett, Cem Tekin, and Mihaela Van Der Schaar · 2021
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The clinical effectiveness and cost-effectiveness of technologies used to visualise the seizure focus in people with refractory epilepsy being considered for surgery: a systematic review and decision-analytical model
J Burch, S Hinde, S Palmer, F Beyer, J Minton, A Marson, U Wieshmann, N Woolacott, and M Soares · 2046
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