Jenna Wiens
Jenna Wiens
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Do no harm: a roadmap for responsible machine learning for health care
J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ...
Nature medicine 25 (9), 1337-1340, 2019
Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology
J Wiens, ES Shenoy
Clinical Infectious Diseases, 2017
Patient risk stratification for hospital-associated c. diff as a time-series classification task
J Wiens, E Horvitz, J Guttag
Advances in Neural Information Processing Systems 25, 2012
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions
J Wiens, J Guttag, E Horvitz
Journal of the American Medical Informatics Association 21 (4), 699-706, 2014
A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers
J Oh, M Makar, C Fusco, R McCaffrey, K Rao, EE Ryan, L Washer, ...
Infection Control and Hospital Epidemiology 39 (4), 425-433, 2018
A framework for effective application of machine learning to microbiome-based classification problems
BD Topçuoğlu, NA Lesniak, MT Ruffin IV, J Wiens, PD Schloss
MBio 11 (3), e00434-20, 2020
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens
KDD'18 Proceedings of the 24th ACM SIGKDD International Conference on …, 2018
Active learning applied to patient-adaptive heartbeat classification
J Wiens, J Guttag
Advances in neural information processing systems 23, 2010
Patient risk stratification with time-varying parameters: a multitask learning approach
J Wiens, J Guttag, E Horvitz
The Journal of Machine Learning Research 17 (1), 2797-2819, 2016
Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile
J Wiens, WN Campbell, ES Franklin, JV Guttag, E Horvitz
Open forum infectious diseases 1 (2), ofu045, 2014
Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19
K Singh, TS Valley, S Tang, BY Li, F Kamran, MW Sjoding, J Wiens, ...
Annals of the American Thoracic Society 18 (7), 1129-1137, 2021
Machine learning for patient risk stratification for acute respiratory distress syndrome
D Zeiberg, T Prahlad, BK Nallamothu, TJ Iwashyna, J Wiens, MW Sjoding
PloS one 14 (3), e0214465, 2019
Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection
BY Li, J Oh, VB Young, K Rao, J Wiens
Open forum infectious diseases 6 (5), ofz186, 2019
Automatically recognizing on-ball screens
A McQueen, J Wiens, J Guttag
2014 MIT Sloan Sports Analytics Conference, 2014
Shapley flow: A graph-based approach to interpreting model predictions
J Wang, J Wiens, S Lundberg
International Conference on Artificial Intelligence and Statistics, 721-729, 2021
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
J Oh, J Wang, J Wiens
Proceedings of the 3rd Machine Learning for Health Care (MLHC), 2018
Heart sound classification based on temporal alignment techniques
JJG Ortiz, CP Phoo, J Wiens
2016 computing in cardiology conference (CinC), 589-592, 2016
Deep reinforcement learning for closed-loop blood glucose control
I Fox, J Lee, R Pop-Busui, J Wiens
Machine Learning for Healthcare Conference, 508-536, 2020
Diagnosing bias in data-driven algorithms for healthcare
J Wiens, WN Price, MW Sjoding
Nature medicine 26 (1), 25-26, 2020
The number needed to benefit: estimating the value of predictive analytics in healthcare
VX Liu, DW Bates, J Wiens, NH Shah
Journal of the American Medical Informatics Association 26 (12), 1655-1659, 2019
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