| Performance of a machine learning algorithm using electronic health record data to predict postoperative complications and report on a mobile platform Y Ren, TJ Loftus, S Datta, MM Ruppert, Z Guan, S Miao, B Shickel, Z Feng, ... JAMA network open 5 (5), e2211973-e2211973, 2022 | 81 | 2022 |
| Reinforcement learning in surgery S Datta, Y Li, MM Ruppert, Y Ren, B Shickel, T Ozrazgat-Baslanti, ... Surgery 170 (1), 329-332, 2021 | 47 | 2021 |
| Added value of intraoperative data for predicting postoperative complications: the MySurgeryRisk PostOp extension S Datta, TJ Loftus, MM Ruppert, C Giordano, GR Upchurch Jr, P Rashidi, ... Journal of Surgical Research 254, 350-363, 2020 | 42 | 2020 |
| Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction S Datta, VA Dev, MR Eden Computers & Chemical Engineering 106, 690-698, 2017 | 25 | 2017 |
| Optimizing predictive strategies for acute kidney injury after major vascular surgery AC Filiberto, T Ozrazgat-Baslanti, TJ Loftus, YC Peng, S Datta, P Efron, ... Surgery 170 (1), 298-303, 2021 | 14 | 2021 |
| Developing non-linear rate constant QSPR using decision trees and multi-gene genetic programming S Datta, VA Dev, MR Eden Computers & Chemical Engineering 127, 150-157, 2019 | 14 | 2019 |
| Comparison of tree based ensemble machine learning methods for prediction of rate constant of Diels-Alder reaction VA Dev, S Datta, NG Chemmangattuvalappil, MR Eden Computer Aided Chemical Engineering 40, 997-1002, 2017 | 14 | 2017 |
| Data mining and regression algorithms for the development of a QSPR model relating solvent structure and ibuprofen crystal morphology S Datta, RH Herring III, MR Eden Computer Aided Chemical Engineering 37, 1439-1444, 2015 | 10 | 2015 |
| Developing QSPR for predicting DNA drug binding affinity of 9-Anilinoacridine derivatives using correlation-based adaptive LASSO algorithm S Datta, VA Dev, MR Eden Computer Aided Chemical Engineering 40, 2767-2772, 2017 | 8 | 2017 |
| Development of QSPR model relating solvent structure to crystal morphology JC Haser, RH Herring III, S Datta, MR Eden Computer Aided Chemical Engineering 34, 321-326, 2014 | 7 | 2014 |
| Using correlation based adaptive LASSO algorithm to develop QSPR of antitumour agents for DNA–drug binding prediction S Datta, VA Dev, MR Eden Computers & Chemical Engineering 122, 258-264, 2019 | 5 | 2019 |
| Application of deep interpolation network for clustering of physiologic time series Y Li, Y Ren, TJ Loftus, S Datta, M Ruppert, Z Guan, D Wu, P Rashidi, ... arXiv preprint arXiv:2004.13066, 2020 | 3 | 2020 |
| Membrane development from banana peel fibers for waste water treatment at low cost S Datta, S Karmoker, MT Sowgath Conference, Environmental division, 2012 | 3 | 2012 |
| Physiologic signatures within six hours of hospitalization identify acute illness phenotypes AB Yuanfang Ren,Tyler J. Loftus,Yanjun Li,Ziyuan Guan,Matthew M. Ruppert ... PLOS Digital Health 1 (10), 2022 | 2 | 2022 |
| Relating Reaction Rate Constant to Structures of Reactants and Solvent Using a Hybrid GA-DT Approach S Datta, VA Dev, MR Eden Computer Aided Chemical Engineering 38, 2049-2054, 2016 | 2 | 2016 |
| Interpretable Multi-Task Deep Neural Networks for Dynamic Predictions of Postoperative Complications. B Shickel, TJ Loftus, S Datta, T Ozrazgat-Baslanti, A Bihorac, P Rashidi arXiv preprint arXiv, 2004 | 1 | 2004 |
| Computer-Aided Analysis of Protease Catalytic Activity to Enhance Process and Product Development A Ali, AC Massey, S Datta, A Adamczyk 2019 AIChE Annual Meeting, 2019 | | 2019 |
| Computational Exploration of Peptide Hydrolysis By Serine Protease: Combined Reaction Path and Process Variable Analysis A Ali, AC Massey, S Datta, A Adamczyk 2019 AIChE Annual Meeting, 2019 | | 2019 |
| Machine Learning Algorithms for QSPR/QSAR Predictive Model Development Involving High-Dimensional Data S Datta Auburn University, 2019 | | 2019 |
| QSAR Study of Combretastatin-like Chalcones As Cancer Cell Growth Inhibitors Using Linear and Non-Linear Machine Learning Approaches S Datta, M Eden 2018 AIChE Annual Meeting, 2018 | | 2018 |
Dr. Mario Richard EdenDean of Engineering and McMillan Professor of Chemical Engineering, Auburn Universityایمیل تأیید شده در auburn.edu