| Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification PA Stokes, P Rath, T Possidente, M He, S Purcell, DS Manoach, ... Sleep 46 (1), zsac223, 2023 | 12 | 2023 |
| Prediction-constrained markov models for medical time series with missing data and few labels P Rath, G Hope, K Heuton, EB Sudderth, MC Hughes NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022 | 3 | 2022 |
| Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint P Rath, M Hughes International Conference on Artificial Intelligence and Statistics, 4895-4914, 2022 | 1 | 2022 |
| Optimizing clinical early warning models to meet false alarm constraints P Rath, MC Hughes 1st Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2021 | 1 | 2021 |
| Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models M Wojnowicz, P Rath, E Miller, J Miller, C Hancock, M O'Donovan, ... arXiv preprint arXiv:2401.14973, 2024 | | 2024 |
| 0999 Characterizing Clinical Population Differences in Transient Oscillation Features in the Sleep EEG PA Stokes, P Rath, DS Manoach, R Stickgold, MJ Prerau Sleep 41 (suppl_1), A370-A370, 2018 | | 2018 |
| Ensemble tree classifier to identify root causes of false alarms at hospital level CL Swisher, P Rath, RE Gregg, R Firoozabadi, A Nagendra, ... Journal of Electrocardiology 50 (6), 868, 2017 | | 2017 |
| Predicting Patient Outcomes from Time Series with Missing Data Via a Semi-Supervised Hidden Markov Model P Rath, G Hope, A Lobo, EB Sudderth, MC Hughes Available at SSRN 4930177, 0 | | |
| Semi-supervised Ordinal Regression via Cumulative Link Models for Predicting In-Hospital Length-of-Stay AA Lobo, P Rath, MC Hughes ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 0 | | |
