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Martin G. Seneviratne
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2020 – today
- 2024
- [i7]Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai:
Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction. CoRR abs/2407.19359 (2024) - 2022
- [j5]Selen Bozkurt
, Christopher J. Magnani, Martin G. Seneviratne, James D. Brooks, Tina Hernandez-Boussard:
Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage. Frontiers Digit. Health 4: 793316 (2022) - [j4]Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23: 226:1-226:61 (2022) - [c8]Tao Tu, Eric Loreaux, Emma Chesley, Ádám D. Lelkes, Paul Gamble, Mathias Bellaiche, Martin Seneviratne, Ming-Jun Chen:
Automated LOINC Standardization Using Pre-trained Large Language Models. ML4H@NeurIPS 2022: 343-355 - [c7]Daniel Lopez Martinez, Alex Yakubovich, Martin Seneviratne, Ádám D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen:
Instability in clinical risk stratification models using deep learning. ML4H@NeurIPS 2022: 552-565 - [c6]Madhurima Vardhan, Narayan Hegde, Srujana Merugu, Shantanu Prabhat, Deepak Nathani, Martin Seneviratne, Nur Muhammad, Pranay Reddy, Sriram Lakshminarasimhan, Rahul Singh, Karina Lorenzana, Eshan Motwani, Partha Talukdar, Aravindan Raghuveer:
Walking with PACE - Personalized and Automated Coaching Engine. UMAP 2022: 57-68 - [i6]Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Mingjun Chen:
Boosting the interpretability of clinical risk scores with intervention predictions. CoRR abs/2207.02941 (2022) - [i5]Daniel Lopez Martinez, Alex Yakubovich, Martin Seneviratne, Ádám D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen:
Instability in clinical risk stratification models using deep learning. CoRR abs/2211.10828 (2022) - [i4]Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Kumar Tanwani, Heather Cole-Lewis, Stephen Pfohl
Large Language Models Encode Clinical Knowledge. CoRR abs/2212.13138 (2022) - 2021
- [j3]Subhrajit Roy, Diana Mincu, Eric Loreaux, Anne Mottram, Ivan Protsyuk, Natalie Harris, Yuan Xue, Jessica Schrouff, Hugh Montgomery
Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing. J. Am. Medical Informatics Assoc. 28(9): 1936-1946 (2021) - [c5]Diana Mincu, Eric Loreaux, Shaobo Hou, Sebastien Baur, Ivan Protsyuk, Martin Seneviratne, Anne Mottram, Nenad Tomasev, Alan Karthikesalingam, Jessica Schrouff:
Concept-based model explanations for electronic health records. CHIL 2021: 36-46 - [i3]Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai:
BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. CoRR abs/2107.08189 (2021) - 2020
- [j2]Mehr Kashyap
Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network. J. Am. Medical Informatics Assoc. 27(6): 877-883 (2020) - [j1]Selen Bozkurt
Reporting of demographic data and representativeness in machine learning models using electronic health records. J. Am. Medical Informatics Assoc. 27(12): 1878-1884 (2020) - [c4]Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai:
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. NeurIPS 2020 - [i2]Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR abs/2011.03395 (2020) - [i1]Sebastien Baur, Shaobo Hou, Eric Loreaux, Diana Mincu, Anne Mottram, Ivan Protsyuk, Nenad Tomasev, Martin G. Seneviratne, Alan Karthikesalingam, Jessica Schrouff:
Concept-based model explanations for Electronic Health Records. CoRR abs/2012.02308 (2020)
2010 – 2019
- 2019
- [c3]Raphael Lenain, Martin G. Seneviratne, Selen Bozkurt
Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer. MedInfo 2019: 1522-1523 - [c2]Martin G. Seneviratne, Michael G. Kahn, Tina Hernandez-Boussard:
Merging heterogeneous clinical data to enable knowledge discovery. PSB 2019: 439-443 - 2018
- [c1]Martin G. Seneviratne, Juan M. Banda, James D. Brooks, Nigam Shah, Tina Hernandez-Boussard:
Identifying cases of metastatic prostate cancer using machine learning on electronic health records. AMIA 2018
Coauthor Index
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last updated on 2024-11-07 20:35 CET by the dblp team
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