Interpretability of machine learning‐based prediction models in healthcare
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Marinka Zitnik | Gregor Stiglic | Katrien Verbert | Nino Fijacko | Primoz Kocbek | Leona Cilar | M. Zitnik | K. Verbert | G. Štiglic | P. Kocbek | N. Fijačko | L. Cilar | Nino Fijačko | Leona Cilar | Primož Kocbek
[1] Ahmed Hosny,et al. Artificial intelligence in radiology , 2018, Nature Reviews Cancer.
[2] Finale Doshi-Velez,et al. A Roadmap for a Rigorous Science of Interpretability , 2017, ArXiv.
[3] Huamin Qu,et al. RuleMatrix: Visualizing and Understanding Classifiers with Rules , 2018, IEEE Transactions on Visualization and Computer Graphics.
[4] Bart Baesens,et al. Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring , 2008, Rule Extraction from Support Vector Machines.
[5] P. Kokol,et al. Comprehensive Decision Tree Models in Bioinformatics , 2012, PloS one.
[6] Scott M. Lundberg,et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery , 2018, Nature Biomedical Engineering.
[7] Christoph Molnar,et al. Interpretable Machine Learning , 2020 .
[8] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[9] Carlos Guestrin,et al. Anchors: High-Precision Model-Agnostic Explanations , 2018, AAAI.
[10] Ivan Bratko,et al. Machine Learning: Between Accuracy and Interpretability , 1997 .
[11] Stephen Todd,et al. A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual , 2019, bioRxiv.
[12] Alex Endert,et al. The State of the Art in Integrating Machine Learning into Visual Analytics , 2017, Comput. Graph. Forum.
[13] Muhammed Besiru Jibrin,et al. Homogenous Ensembles on Data Mining Techniques for Breast Cancer Diagnosis , 2019 .
[14] Huseyin Seker,et al. A soft measurement technique for searching significant subsets of prostate cancer prognostic markers , 2000 .
[15] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[16] Brad Boehmke,et al. Interpretable Machine Learning , 2019 .
[17] Takanori Fujiwara,et al. A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions , 2020, Vis. Informatics.
[18] Jure Leskovec,et al. GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks , 2019, ArXiv.
[19] Antonello Rizzi,et al. Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction , 2019, Soft Computing.
[20] Galit Shmueli,et al. Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers , 2019, Big Data.
[21] Ankur Teredesai,et al. Interpretable Machine Learning in Healthcare , 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).
[22] Yanxin Zhang,et al. Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia , 2019, Comput. Biol. Medicine.
[23] Rich Caruana,et al. Model compression , 2006, KDD '06.
[24] Megan Kurka,et al. Machine Learning Interpretability with H2O Driverless AI , 2019 .
[25] Nuno Cruz,et al. LoBEMS—IoT for Building and Energy Management Systems , 2019, Electronics.
[26] Igor Kononenko,et al. Inductive and Bayesian learning in medical diagnosis , 1993, Appl. Artif. Intell..
[27] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[28] J. Stevenson. The cultural origins of human cognition , 2001 .
[29] Alex John London,et al. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.
[30] Chandan Singh,et al. Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.
[31] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[32] Jure Leskovec,et al. Faithful and Customizable Explanations of Black Box Models , 2019, AIES.
[33] Vili Podgorelec,et al. Using Visual Interpretation of Small Ensembles in Microarray Analysis , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).
[34] Shweta Karun,et al. Comparative Analysis of Prediction Algorithms for Diabetes , 2018, Advances in Intelligent Systems and Computing.
[35] Hui Zhang,et al. Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method. , 2018, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[36] Andre Esteva,et al. A guide to deep learning in healthcare , 2019, Nature Medicine.
[37] Yousef Farhaoui,et al. Data Mining and Machine Learning Approaches and Technologies for Diagnosing Diabetes in Women , 2019, Big Data and Networks Technologies.
[38] Susan M Shortreed,et al. Positive Predictive Values and Potential Success of Suicide Prediction Models. , 2019, JAMA psychiatry.
[39] Jesus J. Caban,et al. Visual analytics in healthcare - opportunities and research challenges , 2015, J. Am. Medical Informatics Assoc..
[40] Carlos Guestrin,et al. Model-Agnostic Interpretability of Machine Learning , 2016, ArXiv.
[41] Lucas Oliveira Batista,et al. A Hybrid Model Based on Fuzzy Rules to Act on the Diagnosed of Autism in Adults , 2019, AIAI.
[42] Kehinde A. Otunaiya,et al. Performance of Datamining Techniques in the Prediction of Chronic Kidney Disease , 2019, Computer Science and Information Technology.
[43] Luciano Floridi,et al. Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation , 2017 .
[44] Lalana Kagal,et al. Explaining Explanations: An Overview of Interpretability of Machine Learning , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).
[45] Hui Zhang,et al. Development of an in silico prediction model for chemical-induced urinary tract toxicity by using naïve Bayes classifier , 2018, Molecular Diversity.
[46] Stefano Nembrini,et al. Bias in the intervention in prediction measure in random forests: illustrations and recommendations , 2018, Bioinform..
[47] Mustafa Suleyman,et al. Key challenges for delivering clinical impact with artificial intelligence , 2019, BMC Medicine.
[48] Jure Leskovec,et al. Interpretable & Explorable Approximations of Black Box Models , 2017, ArXiv.
[49] Lewis J. Frey,et al. Automated detection of altered mental status in emergency department clinical notes: a deep learning approach , 2019, BMC Medical Informatics and Decision Making.
[50] Xun Jia,et al. Clinical implementation of AI technologies will require interpretable AI models. , 2019, Medical physics.
[51] Ali Jamshidi,et al. Machine-learning-based patient-specific prediction models for knee osteoarthritis , 2018, Nature Reviews Rheumatology.
[52] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[53] Samarendra Dandapat,et al. A Weighted SVM Based Approach for Automatic Detection of Posterior Myocardial Infarction Using VCG Signals , 2019, 2019 National Conference on Communications (NCC).
[54] Brian W. Powers,et al. Dissecting racial bias in an algorithm used to manage the health of populations , 2019, Science.
[55] Been Kim,et al. Considerations for Evaluation and Generalization in Interpretable Machine Learning , 2018 .
[56] Muhammad Imran Razzak,et al. Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.
[57] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[58] Shyam Visweswaran,et al. Personalized Modeling for Prediction with Decision-Path Models , 2015, PloS one.
[59] Jimeng Sun,et al. RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records , 2018, IEEE Transactions on Visualization and Computer Graphics.
[60] Chandan Singh,et al. Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.
[61] Jorge A. Gálvez,et al. A Review of Analytics and Clinical Informatics in Health Care , 2014, Journal of Medical Systems.
[62] Xia Hu,et al. Techniques for interpretable machine learning , 2018, Commun. ACM.
[63] Georg Langs,et al. Causability and explainability of artificial intelligence in medicine , 2019, WIREs Data Mining Knowl. Discov..
[64] Matjaz Gams,et al. What makes classification trees comprehensible? , 2016, Expert Syst. Appl..
[65] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[66] Fei Wang,et al. AI in Health: State of the Art, Challenges, and Future Directions , 2019, Yearbook of Medical Informatics.
[67] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[68] Jude W. Shavlik,et al. Using Sampling and Queries to Extract Rules from Trained Neural Networks , 1994, ICML.
[69] Kristin A. Cook,et al. Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .
[70] Tim Miller,et al. Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..
[71] Issam Salman. Heart attack mortality prediction: an application of machine learning methods , 2019 .
[72] Jaime S. Cardoso,et al. Machine Learning Interpretability: A Survey on Methods and Metrics , 2019, Electronics.
[73] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[74] Sherif Sakr,et al. On the interpretability of machine learning-based model for predicting hypertension , 2019, BMC Medical Informatics and Decision Making.
[75] Mohammad Mansouri,et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets , 2018, Nature Biomedical Engineering.
[76] Mateusz Buda,et al. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[77] Sherif Sakr,et al. Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques , 2019, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS).
[78] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[79] Yang Long,et al. Automated General Movement Assessment for Perinatal Stroke Screening in Infants , 2019, Smart Assisted Living.
[80] David C. Kale,et al. Do no harm: a roadmap for responsible machine learning for health care , 2019, Nature Medicine.
[81] Klaus-Robert Müller,et al. "What is relevant in a text document?": An interpretable machine learning approach , 2016, PloS one.
[82] Benoît Frénay,et al. Interpretability of machine learning models and representations: an introduction , 2016, ESANN.
[83] Stan Matwin,et al. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2017, KDD.