| Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network P Petousis, SX Han, D Aberle, AAT Bui Artificial intelligence in medicine 72, 42-55, 2016 | 58 | 2016 |
| Using sequential decision making to improve lung cancer screening performance P Petousis, A Winter, W Speier, DR Aberle, W Hsu, AAT Bui Ieee Access 7, 119403-119419, 2019 | 49 | 2019 |
| A Bayesian model for estimating multi-state disease progression S Shen, SX Han, P Petousis, RE Weiss, F Meng, AAT Bui, W Hsu Computers in biology and medicine 81, 111-120, 2017 | 14 | 2017 |
| Generating reward functions using IRL towards individualized cancer screening P Petousis, SX Han, W Hsu, AAT Bui International Workshop on Artificial Intelligence in Health, 213-227, 2018 | 9 | 2018 |
| TSI-GNN: extending graph neural networks to handle missing data in temporal settings D Gordon, P Petousis, H Zheng, D Zamanzadeh, AAT Bui Frontiers in big Data 4, 693869, 2021 | 6 | 2021 |
| Clinical course and outcome after kidney transplantation in patients with C3 glomerulonephritis due to CFHR5 nephropathy E Frangou, A Varnavidou-Nicolaidou, P Petousis, A Soloukides, ... Nephrology Dialysis Transplantation 34 (10), 1780-1788, 2019 | 5 | 2019 |
| Evaluating the impact of uncertainty on risk prediction: Towards more robust prediction models P Petousis, A Naeim, A Mosleh, W Hsu AMIA Annual Symposium Proceedings 2018, 1461, 2018 | 4 | 2018 |
| Autopopulus: a novel framework for autoencoder imputation on large clinical datasets DJ Zamanzadeh, P Petousis, TA Davis, SB Nicholas, KC Norris, KR Tuttle, ... 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 3 | 2021 |
| Optimizing cancer screening with POMDPs P Petousis University of California, Los Angeles, 2019 | 2 | 2019 |
| A Big Data COVID-19 literature pattern discovery using NLP P Petousis, V Stylianou bioRxiv, 2022.06. 01.494451, 2022 | 1 | 2022 |
| Early prediction of end-stage kidney disease using electronic health record data: a machine learning approach with a 2-year horizon P Petousis, JM Wilson, AV Gelvezon, S Alam, A Jain, L Prichard, ... JAMIA open 7 (1), ooae015, 2024 | | 2024 |
| Towards a framework for interoperability and reproducibility of predictive models A Rahrooh, AO Garlid, K Bartlett, W Coons, P Petousis, W Hsu, AAT Bui Journal of Biomedical Informatics 149, 104551, 2024 | | 2024 |
| Data-driven prediction of continuous renal replacement therapy survival D Zamanzadeh, J Feng, P Petousis, A Vepa, M Sarrafzadeh, ... Research Square, 2023 | | 2023 |
| Automated Dynamic Bayesian Networks for Predicting Acute Kidney Injury Before Onset D Gordon, P Petousis, AO Garlid, K Norris, K Tuttle, SB Nicholas, AAT Bui arXiv preprint arXiv:2304.10175, 2023 | | 2023 |
| WCN23-1183 DISPARITIES IN CHRONIC KIDNEY DISEASE RISKS: DATA FROM THE CURE-CKD COVID-19 REGISTRY S Nicholas, R Follett, T Tacorda, X Wang, D Ruenger, P Petousis, B Zhu, ... Kidney International Reports 8 (3), S453-S454, 2023 | | 2023 |
| Disparities in CKD risks: Data from the cure-CKD COVID-19 sub-registry SB Nicholas, RW Follett, TT Tacorda, X Wang, D Ruenger, P Petousis, ... Journal of the American Society of Nephrology, 84-84, 2021 | | 2021 |
| Using Autoencoders for Imputing Missing Data in eGFR Decline Trajectories of Patients with CKD DJ Zamanzadeh, P Petousis, TA Davis, AO Garlid, X Wang, KC Norris, ... ASN Kidney Week, 2020 | | 2020 |
| PO0528: Predicting Rapid eGFR Decline Using Electronic Health Record (EHR) Data Despite High Missingness in the CURE-CKD Registry TA Davis, P Petousis, DJ Zamanzadeh, X Wang, KC Norris, O Duru, ... ASN Kidney Week, 2020 | | 2020 |
