Roy Adams
Cited by
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Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
R Adams, KE Henry, A Sridharan, H Soleimani, A Zhan, N Rawat, ...
Nature medicine 28 (7), 1455-1460, 2022
Evaluating model robustness and stability to dataset shift
A Subbaswamy, R Adams, S Saria
International conference on artificial intelligence and statistics, 2611-2619, 2021
Impact of a collective intelligence tailored messaging system on smoking cessation: the perspect randomized experiment
RS Sadasivam, EM Borglund, R Adams, BM Marlin, TK Houston
Journal of medical Internet research 18 (11), e285, 2016
Deep phenotyping of Parkinson’s disease
E Dorsey, L Omberg, E Waddell, JL Adams, R Adams, MR Ali, K Amodeo, ...
Journal of Parkinson's Disease 10 (3), 855-873, 2020
Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing
KE Henry, R Adams, C Parent, H Soleimani, A Sridharan, L Johnson, ...
Nature Medicine, 1-8, 2022
A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models
HE Wang, M Landers, R Adams, A Subbaswamy, H Kharrazi, DJ Gaskin, ...
Journal of the American Medical Informatics Association 29 (8), 1323-1333, 2022
rconverse: Moment by moment conversation detection using a mobile respiration sensor
R Bari, RJ Adams, MM Rahman, MB Parsons, EH Buder, S Kumar
Proceedings of the ACM on interactive, mobile, wearable and ubiquitous …, 2018
Towards collaborative filtering recommender systems for tailored health communications
BM Marlin, RJ Adams, R Sadasivam, TK Houston
AMIA annual symposium proceedings 2013, 1600, 2013
PERSPeCT: collaborative filtering for tailored health communications
RJ Adams, RS Sadasivam, K Balakrishnan, RL Kinney, TK Houston, ...
Proceedings of the 8th ACM Conference on Recommender systems, 329-332, 2014
Learning models from data with measurement error: Tackling underreporting
R Adams, Y Ji, X Wang, S Saria
International Conference on Machine Learning, 61-70, 2019
Hierarchical span-based conditional random fields for labeling and segmenting events in wearable sensor data streams
R Adams, N Saleheen, E Thomaz, A Parate, S Kumar, B Marlin
International conference on machine learning, 334-343, 2016
Learning time series detection models from temporally imprecise labels
R Adams, B Marlin
Artificial Intelligence and Statistics, 157-165, 2017
Partial identifiability in discrete data with measurement error
N Finkelstein, R Adams, S Saria, I Shpitser
Uncertainty in Artificial Intelligence, 1798-1808, 2021
Learning Time Series Segmentation Models from Temporally Imprecise Labels
RJ Adams, BM Marlin
The Conference on Uncertainty in Artificial Intelligence (UAI), 2018
Endogenous and exogenous thyrotoxicosis and risk of incident cognitive disorders in older adults
R Adams, ES Oh, S Yasar, CG Lyketsos, JS Mammen
JAMA internal medicine 183 (12), 1324-1331, 2023
Partial identifiability in discrete data with measurement error
N Finkelstein, R Adams, S Saria, I Shpitser
arXiv preprint arXiv:2012.12449, 2020
Parsing wireless electrocardiogram signals with context free grammar conditional random fields
T Nguyen, RJ Adams, A Natarajan, BM Marlin
2016 IEEE Wireless Health (WH), 1-8, 2016
Evaluating adoption, impact, and factors driving adoption for TREWS, a machine learning-based sepsis alerting system
KE Henry, R Adams, C Parent, A Sridharan, L Johnson, DN Hager, ...
medRxiv, 2021
The impact of time series length and discretization on longitudinal causal estimation methods
R Adams, S Saria, M Rosenblum
arXiv preprint arXiv:2011.15099, 2020
1405: assessing clinical use and performance of a machine learning sepsis alert for sex and racial bias
R Adams, K Henry, H Soleimani, N Rawat, M Saheed, E Chen, A Wu, ...
Critical Care Medicine 50 (1), 705, 2022
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