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Unpredictable ML model behavior on unseen data, especially in the health domain, raises serious concerns about its safety as repercussions for mistakes can be fatal.
The health belief model: A decade later
Nancy K Janz and Marshall H Becker · 1984
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R. Rosenthal, H. Rosenthal, and inc Sage Publications · 1991
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Individual comparisons by ranking methods
Frank Wilcoxon · 1992
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The health belief model
Victor J Strecher and Irwin M Rosenstock · 1997
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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mhealthdroid: a novel framework for agile development of mobile health applications
Oresti Banos, Rafael Garcia, Juan A Holgado-Terriza, Miguel Damas, Hector Pomares, Ignacio Rojas, Alejandro Saez, and Claudia Villalonga · 2014
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Bilicam: using mobile phones to monitor newborn jaundice
Lilian De Greef, Mayank Goel, Min Joon Seo, Eric C Larson, James W Stout, James A Taylor, and Shwetak N Patel · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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The mpower study, parkinson disease mobile data collected using researchkit
Brian M Bot, Christine Suver, Elias Chaibub Neto, Michael Kellen, Arno Klein, Christopher Bare, Megan Doerr, Abhishek Pratap, John Wilbanks, E Ray Dorsey, et al · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Beyond prediction: Using big data for policy problems
Susan Athey · 2017
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Audio set: An ontology and human-labeled dataset for audio events
Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
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Characterisation of mental health conditions in social media using informed deep learning
George Gkotsis, Anika Oellrich, Sumithra Velupillai, Maria Liakata, Tim JP Hubbard, Richard JB Dobson, and Rina Dutta · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Biliscreen: smartphone-based scleral jaundice monitoring for liver and pancreatic disorders
Alex Mariakakis, Megan A Banks, Lauren Phillipi, Lei Yu, James Taylor, and Shwetak N Patel · 2017
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Pupilscreen: using smartphones to assess traumatic brain injury
Alex Mariakakis, Jacob Baudin, Eric Whitmire, Vardhman Mehta, Megan A Banks, Anthony Law, Lynn Mcgrath, and Shwetak N Patel · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Christoph Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, et al · 2018
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Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
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Simulation of parkinson movement disorders – kaggle, May 2018
Giorgia · 2018
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
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To trust or not to trust a classifier
Heinrich Jiang, Been Kim, Melody Y. Guan, and Maya R. Gupta · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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A benchmark of medical out of distribution detection
Tianshi Cao, Chinwei Huang, David Yu-Tung Hui, and Joseph Paul Cohen · 2020
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Assessing and mitigating unfairness in credit models with the fairlearn toolkit
Miro Dudík, William Chen, Solon Barocas, Mario Inchiosa, Nick Lewins, Miruna Oprescu, Joy Qiao, Mehrnoosh Sameki, Mario Schlener, Jason Tuo, and Hanna Wallach · 2020
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Security and machine learning in the real world
Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, and Jerry Li · 2020
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Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting, 2020
Siddhartha Gairola, Francis Tom, Nipun Kwatra, and Mohit Jain · 2020
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Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
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Andrey Malinin and Mark Gales · 2018
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Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Detecting out-of-distribution samples using low-order deep features statistics
Igor M. Quintanilha, Roberto de M. E. Filho, José Lezama, Mauricio Delbracio, and Leonardo O. Nunes · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
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Fair regression: Quantitative definitions and reduction-based algorithms
Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu · 2019
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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado, et al · 2019
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Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Multi-task temporal shift attention networks for on-device contactless vitals measurement
Xin Liu, Josh Fromm, Shwetak Patel, and Daniel McDuff · 2020
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Self-supervised learning for generalizable out-of-distribution detection
Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang · 2020
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Quasi-oracle estimation of heterogeneous treatment effects
Xinkun Nie and Stefan Wager · 2020
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On out-of-distribution detection algorithms with deep neural skin cancer classifiers
Andre GC Pacheco, Chandramouli S Sastry, Thomas Trappenberg, Sageev Oore, and Renato A Krohling · 2020
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Mitigating bias in algorithmic hiring: evaluating claims and practices
Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy · 2020
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Detecting out-of-distribution examples with gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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Blood pressure measurements with the optibp smartphone app validated against reference auscultatory measurements
Patrick Schoettker, Jean Degott, Gregory Hofmann, Martin Proença, Guillaume Bonnier, Alia Lemkaddem, Mathieu Lemay, Raoul Schorer, Urvan Christen, Jean-François Knebel, et al · 2020
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Deep learning in mental health outcome research: a scoping review
Chang Su, Zhenxing Xu, Jyotishman Pathak, and Fei Wang · 2020
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From development to deployment: dataset shift, causality, and shift-stable models in health ai
Adarsh Subbaswamy and Suchi Saria · 2020
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Calibrating healthcare ai: Towards reliable and interpretable deep predictive models
Jayaraman J Thiagarajan, Prasanna Sattigeri, Deepta Rajan, and Bindya Venkatesh · 2020
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Self-supervised out-of-distribution detection in brain ct scans
Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, and Nassir Navab · 2020
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Deep learning identifies digital biomarkers for self-reported parkinson’s disease
Hanrui Zhang, Kaiwen Deng, Hongyang Li, Roger L Albin, and Yuanfang Guan · 2020
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Hybrid models for open set recognition
Hongjie Zhang, Ang Li, Jie Guo, and Yanwen Guo · 2020
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Face Research Lab London Set
Lisa DeBruine and Benedict Jones · 2021
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A dataset of lung sounds recorded from the chest wall using an electronic stethoscope
Mohammad Fraiwan, Luay Fraiwan, Basheer Khassawneh, and Ali Ibnian · 2021
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Smartphone camera oximetry in an induced hypoxemia study
Jason S Hoffman, Varun Viswanath, Xinyi Ding, Matthew J Thompson, Eric C Larson, Shwetak N Patel, and Edward Wang · 2021
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Density of states estimation for out of distribution detection
Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, and Joshua Dillon · 2021
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Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, et al · 2021
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Crowdsourcing digital health measures to predict parkinson’s disease severity: the parkinson’s disease digital biomarker dream challenge
Solveig K Sieberts, Jennifer Schaff, Marlena Duda, Bálint Ármin Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, et al · 2021
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Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2021
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