Interpretable Machine Learning in Healthcare

This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.

[1]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[2]  William Yang Wang.  “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection , 2017, ACL.

[3]  Johannes Gehrke,et al.  Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.

[4]  Pedro M. Domingos.  The Role of Occam's Razor in Knowledge Discovery , 1999, Data Mining and Knowledge Discovery.

[5]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[6]  Eric P. Xing,et al.  Contextual Explanation Networks , 2017, J. Mach. Learn. Res..

[7]  Erik Strumbelj,et al.  Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.

[8]  Cynthia Rudin,et al.  Supersparse linear integer models for optimized medical scoring systems , 2015, Machine Learning.

[9]  Karen M. Feigh,et al.  Learning From Explanations Using Sentiment and Advice in RL , 2017, IEEE Transactions on Cognitive and Developmental Systems.

[10]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[11]  R. Caruana,et al.  Detecting Bias in Black-Box Models Using Transparent Model Distillation. , 2017 .

[12]  Mary S. Dietrich,et al.  Participatory design of probability-based decision support tools for in-hospital nurses , 2017, AMIA.

[13]  Jaideep Srivastava,et al.  Link Prediction Across Multiple Social Networks , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[14]  T. Murdoch,et al.  The inevitable application of big data to health care. , 2013, JAMA.

[15]  Cynthia Rudin,et al.  Falling Rule Lists , 2014, AISTATS.

[16]  J. Stevenson.  The cultural origins of human cognition , 2001 .

[17]  Todd Kulesza,et al.  Personalizing machine learning systems with explanatory debugging , 2014 .

[18]  David Weinberger,et al.  Accountability of AI Under the Law: The Role of Explanation , 2017, ArXiv.

[19]  Jenna Burrell,et al.  How the machine ‘thinks’: Understanding opacity in machine learning algorithms , 2016 .

[20]  Francesco Bonchi,et al.  Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining , 2016, KDD.

[21]  Susmita Sur-Kolay,et al.  Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare , 2015, IEEE Journal of Biomedical and Health Informatics.

[22]  Gunasekaran Manogaran,et al.  A survey of big data architectures and machine learning algorithms in healthcare , 2017 .

[23]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[24]  Alex Alves Freitas,et al.  Comprehensible classification models: a position paper , 2014, SKDD.

[25]  Toru Nakamura,et al.  I Read but Don't Agree: Privacy Policy Benchmarking using Machine Learning and the EU GDPR , 2018, WWW.

[26]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[27]  Ananthram Swami,et al.  The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).

[28]  Xin Yan,et al.  Linear Regression Analysis: Theory and Computing , 2009 .

[29]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[30]  Olatz Arbelaitz,et al.  A new algorithm to build consolidated trees: study of the error rate and steadiness , 2004, Intelligent Information Systems.