Predicting chronic disease hospitalizations from electronic health records: an interpretable classification approach TS Brisimi, T Xu, T Wang, W Dai, WG Adams, IC Paschalidis Proceedings of the IEEE 106 (4), 690-707, 2018 | 99 | 2018 |
Designing metabolic division of labor in microbial communities M Thommes, T Wang, Q Zhao, IC Paschalidis, D Segrè MSystems 4 (2), 10.1128/msystems. 00263-18, 2019 | 96 | 2019 |
Early prediction of level-of-care requirements in patients with COVID-19 B Hao, S Sotudian, T Wang, T Xu, Y Hu, A Gaitanidis, K Breen, ... Elife 9, e60519, 2020 | 64 | 2020 |
Predicting diabetes-related hospitalizations based on electronic health records TS Brisimi, T Xu, T Wang, W Dai, IC Paschalidis Statistical methods in medical research 28 (12), 3667-3682, 2019 | 33 | 2019 |
Predictive models of mortality for hospitalized patients with COVID-19: retrospective cohort study T Wang, A Paschalidis, Q Liu, Y Liu, Y Yuan, IC Paschalidis JMIR medical informatics 8 (10), e21788, 2020 | 15 | 2020 |
Prescriptive analytics for reducing 30-day hospital readmissions after general surgery D Bertsimas, ML Li, IC Paschalidis, T Wang PloS one 15 (9), e0238118, 2020 | 13 | 2020 |
A joint sparse clustering and classification approach with applications to hospitalization prediction T Xu, TS Brisimi, T Wang, W Dai, IC Paschalidis 2016 IEEE 55th conference on decision and control (CDC), 4566-4571, 2016 | 12 | 2016 |
Predictive models of pregnancy based on data from a preconception cohort study JJ Yland, T Wang, Z Zad, SK Willis, TR Wang, AK Wesselink, T Jiang, ... Human Reproduction 37 (3), 565-576, 2022 | 11 | 2022 |
Convergence of parameter estimates for regularized mixed linear regression models T Wang, IC Paschalidis 2019 IEEE 58th Conference on Decision and Control (CDC), 3664-3669, 2019 | 8 | 2019 |
Prescriptive cluster-dependent support vector machines with an application to reducing hospital readmissions T Wang, IC Paschalidis 2019 18th European Control Conference (ECC), 1182-1187, 2019 | 7 | 2019 |
Designing metabolic division of labor in microbial communities. mSystems 4: e00263-18 M Thommes, T Wang, Q Zhao, IC Paschalidis, D Segrè DOI 10, 00263-18, 2019 | 7 | 2019 |
VAP risk index: Early prediction and hospital phenotyping of ventilator-associated pneumonia using machine learning A Samadani, T Wang, K van Zon, LA Celi Artificial Intelligence in Medicine 146, 102715, 2023 | 6 | 2023 |
Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records Z Zad, VS Jiang, AT Wolf, T Wang, JJ Cheng, IC Paschalidis, ... Frontiers in Endocrinology 15, 1298628, 2024 | 3 | 2024 |
Predicting Antimicrobial Resistance in the Intensive Care Unit T Wang, KR Hansen, J Loving, IC Paschalidis, H van Aggelen, E Simhon arXiv preprint arXiv:2111.03575, 2021 | 1 | 2021 |
Data analytics and optimization methods in biomedical systems: from microbes to humans T Wang | 1 | 2020 |
Designing metabolic division of labor in microbial communities T Meghan, W Taiyao, Q Zhao, IC Paschalidis, D Segrè MSystems 4 (2), 2019 | 1 | 2019 |
Strong consistency of parameter estimates for purely explosive autoregressive models with exogenous inputs T Wang, B Qi Proceedings of the 33rd Chinese Control Conference, 6588-6592, 2014 | 1 | 2014 |
Prediction of deep molecular response in chronic myeloid leukemia using supervised machine learning models Z Zad, S Bonecker, T Wang, I Zalcberg, GT Stelzer, B Sabioni, ... Leukemia research 141, 107502, 2024 | | 2024 |
Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations K Herbst, T Wang, EJ Forchielli, M Thommes, IC Paschalidis, D Segrè Communications Biology 7 (1), 407, 2024 | | 2024 |
Incremental clustering and hierarchy formation system for clinical decision support (cds) system development and method of operation thereof AAA Samadani, W Taiyao, C Van Zon US Patent App. 17/978,482, 2023 | | 2023 |