Martin G. Seneviratne

According to our database, Martin G. Seneviratne authored at least 16 papers between 2018 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

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

J. Mach. Learn. Res., 2022

Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage.

Selen Bozkurt, Christopher J. Magnani, Martin G. Seneviratne, James D. Brooks, Tina Hernandez-Boussard

Frontiers Digit. Health, 2022

Large Language Models Encode Clinical Knowledge.

Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Kumar Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Schärli, Aakanksha Chowdhery, Philip Andrew Mansfield, Blaise Agüera y Arcas, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle K. Barral, Christopher Semturs, Alan Karthikesalingam, Vivek Natarajan

CoRR, 2022

Boosting the interpretability of clinical risk scores with intervention predictions.

Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Mingjun Chen

CoRR, 2022

Walking with PACE - Personalized and Automated Coaching Engine.

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

Proceedings of the UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization, Barcelona, Spain, July 4, 2022

Automated LOINC Standardization Using Pre-trained Large Language Models.

Tao Tu, Eric Loreaux, Emma Chesley, Ádám D. Lelkes, Paul Gamble, Mathias Bellaiche, Martin Seneviratne, Ming-Jun Chen

Proceedings of the Machine Learning for Health, 2022

Instability in clinical risk stratification models using deep learning.

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

Proceedings of the Machine Learning for Health, 2022

2021

Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

Subhrajit Roy, Diana Mincu, Eric Loreaux, Anne Mottram, Ivan Protsyuk, Natalie Harris, Yuan Xue, Jessica Schrouff, Hugh Montgomery, Alistair Connell, Nenad Tomasev, Alan Karthikesalingam, Martin Seneviratne

J. Am. Medical Informatics Assoc., 2021

BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark.

Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai

CoRR, 2021

Concept-based model explanations for electronic health records.

Diana Mincu, Eric Loreaux, Shaobo Hou, Sebastien Baur, Ivan Protsyuk, Martin Seneviratne, Anne Mottram, Nenad Tomasev, Alan Karthikesalingam, Jessica Schrouff

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020

Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Mehr Kashyap, Martin G. Seneviratne, Juan M. Banda, Thomas Falconer, Borim Ryu, Sooyoung Yoo, George Hripcsak, Nigam H. Shah

J. Am. Medical Informatics Assoc., 2020

Reporting of demographic data and representativeness in machine learning models using electronic health records.

Selen Bozkurt, Eli M. Cahan, Martin G. Seneviratne, Ran Sun, Juan Antonio Lossio-Ventura, John P. A. Ioannidis, Tina Hernandez-Boussard

J. Am. Medical Informatics Assoc., 2020

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction.

Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Merging heterogeneous clinical data to enable knowledge discovery.

Martin G. Seneviratne, Michael G. Kahn, Tina Hernandez-Boussard

Proceedings of the Biocomputing 2019: Proceedings of the Pacific Symposium, 2019

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Raphael Lenain, Martin G. Seneviratne, Selen Bozkurt, Douglas W. Blayney, James D. Brooks, Tina Hernandez-Boussard

Proceedings of the MEDINFO 2019: Health and Wellbeing e-Networks for All, 2019

2018

Identifying cases of metastatic prostate cancer using machine learning on electronic health records.

Martin G. Seneviratne, Juan M. Banda, James D. Brooks, Nigam Shah, Tina Hernandez-Boussard

Proceedings of the AMIA 2018, 2018