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Books and Theses
- 2014
[b1]Jenna Wiens:
Learning to prevent healthcare-associated infections: leveraging data across time and space to improve local predictions. Massachusetts Institute of Technology, Cambridge, MA, USA, 2014
Journal Articles
- 2024
- [j12]Harry Rubin-Falcone
, Joyce M. Lee, Jenna Wiens:
Learning control-ready forecasters for Blood Glucose Management. Comput. Biol. Medicine 180: 108995 (2024) - 2022
- [j11]Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding:
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. J. Am. Medical Informatics Assoc. 29(6): 1060-1068 (2022) - [j10]Saige Rutherford, Pascal Sturmfels, Mike Angstadt, Jasmine L. Hect, Jenna Wiens, Marion I. van den Heuvel, Dustin Scheinost, Chandra Sekhar Sripada, Moriah E. Thomason:
Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics 20(1): 173-185 (2022) - 2021
- [j9]Begüm D. Topçuoglu, Zena Lapp, Kelly L. Sovacool, Evan S. Snitkin, Jenna Wiens, Patrick D. Schloss:
mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. J. Open Source Softw. 6(61): 3073 (2021) - [j8]Fahad Kamran, Victor C. Le, Adam Frischknecht, Jenna Wiens, Kathleen H. Sienko:
Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes. Sensors 21(13): 4469 (2021) - [j7]Jeremiah Hauth, Safa Jabri, Fahad Kamran, Eyoel W. Feleke, Kaleab Nigusie, Lauro V. Ojeda, Shirley Handelzalts, Linda Nyquist, Neil B. Alexander, Xun Huan, Jenna Wiens, Kathleen H. Sienko:
Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units. Sensors 21(14): 4661 (2021) - 2020
- [j6]Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens:
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Medical Informatics Assoc. 27(12): 1921-1934 (2020) - 2019
- [j5]Vincent X. Liu, David W. Bates, Jenna Wiens, Nigam H. Shah:
The number needed to benefit: estimating the value of predictive analytics in healthcare. J. Am. Medical Informatics Assoc. 26(12): 1655-1659 (2019) - 2016
- [j4]Jenna Wiens, John V. Guttag, Eric Horvitz:
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. J. Mach. Learn. Res. 17: 79:1-79:23 (2016) - [j3]Jenna Wiens, Byron C. Wallace:
Editorial: special issue on machine learning for health and medicine. Mach. Learn. 102(3): 305-307 (2016) - 2015
- [j2]Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres, Amir-massoud Farahmand, Mark Fox, Lutz Frommberger, Sam Ganzfried, Yolanda Gil, Sébastien Guillet, Lawrence E. Hunter, Arnav Jhala, Kristian Kersting, George Dimitri Konidaris, Freddy Lécué, Sheila A. McIlraith, Sriraam Natarajan, Zeinab Noorian, David Poole, Rémi Ronfard, Alessandro Saffiotti, Arash Shaban-Nejad, Biplav Srivastava, Gerald Tesauro, Rosario Uceda-Sosa, Guy Van den Broeck, Martijn van Otterlo, Byron C. Wallace, Paul Weng, Jenna Wiens, Jie Zhang:
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - 2014
- [j1]Jenna Wiens, John V. Guttag, Eric Horvitz:
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Medical Informatics Assoc. 21(4): 699-706 (2014)
Conference and Workshop Papers
- 2024
- [c35]Fahad Kamran, Maggie Makar, Jenna Wiens:
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. AISTATS 2024: 3727-3735 - [c34]Trenton Chang, Jenna Wiens:
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. ICML 2024 - 2023
- [c33]Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens:
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. AAAI 2023: 9650-9657 - [c32]Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens:
Denoising Autoencoders for Learning from Noisy Patient-Reported Data. CHIL 2023: 393-409 - [c31]Donna Tjandra, Jenna Wiens:
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. CHIL 2023: 477-497 - [c30]Erkin Ötles, Brian T. Denton, Jenna Wiens:
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. MLHC 2023: 529-547 - [c29]Shengpu Tang, Jenna Wiens:
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. NeurIPS 2023 - 2022
- [c28]Trenton Chang, Michael W. Sjoding, Jenna Wiens:
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. MLHC 2022: 343-390 - [c27]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS 2022 - [c26]Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens:
Learning Concept Credible Models for Mitigating Shortcuts. NeurIPS 2022 - 2021
- [c25]Fahad Kamran, Jenna Wiens:
Estimating Calibrated Individualized Survival Curves with Deep Learning. AAAI 2021: 240-248 - [c24]Donna Tjandra, Yifei He, Jenna Wiens:
A Hierarchical Approach to Multi-Event Survival Analysis. AAAI 2021: 591-599 - [c23]Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg:
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. AISTATS 2021: 721-729 - [c22]Shengpu Tang, Jenna Wiens:
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. MLHC 2021: 2-35 - [c21]Erkin Ötles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens:
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. MLHC 2021: 506-534 - 2020
- [c20]Harry Rubin-Falcone, Ian Fox, Jenna Wiens:
Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction. KDH@ECAI 2020: 105-109 - [c19]Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens:
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. ICML 2020: 9387-9396 - [c18]Ian Fox, Joyce M. Lee, Rodica Pop-Busui, Jenna Wiens:
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. MLHC 2020: 508-536 - [c17]Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens:
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. MLHC 2020: 750-782 - 2019
- [c16]Ian Fox, Jenna Wiens:
Advocacy Learning: Learning through Competition and Class-Conditional Representations. IJCAI 2019: 2315-2321 - [c15]Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens:
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. MLHC 2019: 27-52 - 2018
- [c14]Maggie Makar, John V. Guttag, Jenna Wiens:
Learning the Probability of Activation in the Presence of Latent Spreaders. AAAI 2018: 134-141 - [c13]Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens:
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. KDD 2018: 1387-1395 - [c12]Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens:
Learning Credible Models. KDD 2018: 2417-2426 - [c11]Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sekhar Sripada, Jenna Wiens:
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images. MLHC 2018: 295-311 - [c10]Jeeheh Oh, Jiaxuan Wang, Jenna Wiens:
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. MLHC 2018: 332-347 - 2017
- [c9]Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens:
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. AMIA 2017 - [c8]Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens:
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. KDD 2017: 155-164 - 2016
- [c7]Jose Javier Gonzalez Ortiz, Cheng Perng Phoo, Jenna Wiens:
Heart Sound Classification Based on Temporal Alignment Techniques. CinC 2016 - 2015
- [c6]Abhishek Bafna, Jenna Wiens:
Learning Useful Abstractions from the Web. AMIA 2015 - [c5]Abhishek Bafna, Jenna Wiens:
Automated Feature Learning: Mining Unstructured Data for Useful Abstractions. ICDM 2015: 703-708 - 2014
- [c4]Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens:
Preface. AAAI Workshop: Modern Artificial Intelligence for Health Analytics 2014 - 2012
- [c3]Jenna Wiens, John V. Guttag, Eric Horvitz:
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. NIPS 2012: 476-484 - 2011
- [c2]Jenna Wiens, John V. Guttag:
Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. EMBC 2011: 5876-5879 - 2010
- [c1]Jenna Wiens, John V. Guttag:
Active Learning Applied to Patient-Adaptive Heartbeat Classification. NIPS 2010: 2442-2450
Editorship
- 2020
- [e6]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. Proceedings of Machine Learning Research 126, PMLR 2020 [contents] - 2019
- [e5]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. Proceedings of Machine Learning Research 106, PMLR 2019 [contents] - 2018
- [e4]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2018, 17-18 August 2018, Palo Alto, California. Proceedings of Machine Learning Research 85, PMLR 2018 [contents] - 2017
- [e3]Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017. Proceedings of Machine Learning Research 68, PMLR 2017 [contents] - 2016
- [e2]Finale Doshi-Velez, Jim Fackler, David C. Kale, Byron C. Wallace, Jenna Wiens:
Proceedings of the 1st Machine Learning in Health Care, MLHC 2016, Los Angeles, CA, USA, August 19-20, 2016. JMLR Workshop and Conference Proceedings 56, JMLR.org 2016 [contents] - 2014
- [e1]Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens:
Modern Artificial Intelligence for Health Analytics, Papers from the 2014 AAAI Workshop, Québec City, Québec, Canada, July 27, 2014. AAAI Technical Report WS-14-08, AAAI 2014 [contents]
Informal and Other Publications
- 2024
- [i29]Trenton Chang, Jenna Wiens:
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. CoRR abs/2406.18865 (2024) - [i28]Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey, Jenna Wiens:
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks. CoRR abs/2407.14509 (2024) - 2023
- [i27]Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens:
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose. CoRR abs/2304.08593 (2023) - [i26]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR abs/2305.01738 (2023) - [i25]Donna Tjandra, Jenna Wiens:
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. CoRR abs/2307.04868 (2023) - [i24]Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo:
Leveraging Factored Action Spaces for Off-Policy Evaluation. CoRR abs/2307.07014 (2023) - [i23]Erkin Ötles, Brian T. Denton, Jenna Wiens:
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. CoRR abs/2308.05619 (2023) - [i22]Shengpu Tang, Jenna Wiens:
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. CoRR abs/2310.17146 (2023) - 2022
- [i21]Trenton Chang, Michael W. Sjoding, Jenna Wiens:
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. CoRR abs/2208.01127 (2022) - 2021
- [i20]Shengpu Tang, Jenna Wiens:
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings. CoRR abs/2107.11003 (2021) - [i19]Erkin Ötles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens:
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation. CoRR abs/2107.13964 (2021) - [i18]Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W. Sjoding:
Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure. CoRR abs/2108.12530 (2021) - 2020
- [i17]Jiaxuan Wang, Jenna Wiens:
AdaSGD: Bridging the gap between SGD and Adam. CoRR abs/2006.16541 (2020) - [i16]Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens:
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. CoRR abs/2007.12678 (2020) - [i15]Ian Fox, Joyce M. Lee, Rodica Pop-Busui, Jenna Wiens:
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. CoRR abs/2009.09051 (2020) - [i14]Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens:
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. CoRR abs/2009.10132 (2020) - [i13]Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg:
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. CoRR abs/2010.14592 (2020) - 2019
- [i12]Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens:
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series. CoRR abs/1906.02898 (2019) - [i11]Tian Bao, Brooke N. Klatt, Susan L. Whitney, Kathleen H. Sienko, Jenna Wiens:
Automatically Evaluating Balance: A Machine Learning Approach. CoRR abs/1906.05657 (2019) - [i10]Ian Fox, Jenna Wiens:
Advocacy Learning: Learning through Competition and Class-Conditional Representations. CoRR abs/1908.02723 (2019) - 2018
- [i9]Dev Goyal, Zeeshan Syed, Jenna Wiens:
Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment. CoRR abs/1803.00744 (2018) - [i8]Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens:
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA. CoRR abs/1803.02940 (2018) - [i7]Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens:
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories. CoRR abs/1806.05357 (2018) - [i6]Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sekhar Sripada, Jenna Wiens:
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images. CoRR abs/1808.04362 (2018) - [i5]Jeeheh Oh, Jiaxuan Wang, Jenna Wiens:
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks. CoRR abs/1808.06725 (2018) - [i4]Eli Sherman, Hitinder S. Gurm, Ulysses J. Balis, Scott R. Owens, Jenna Wiens:
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale. CoRR abs/1811.12520 (2018) - 2017
- [i3]Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens:
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data. CoRR abs/1703.02144 (2017) - [i2]Jiaxuan Wang, Jeeheh Oh, Jenna Wiens:
Learning Credible Models. CoRR abs/1711.03190 (2017) - [i1]Maggie Makar, John V. Guttag, Jenna Wiens:
Learning the Probability of Activation in the Presence of Latent Spreaders. CoRR abs/1712.00643 (2017)
Coauthor Index
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