| Insights from a machine learning model for predicting the hospital Length of Stay (LOS) at the time of admission L Turgeman, JH May, R Sciulli Expert Systems with Applications 78, 376-385, 2017 | 138 | 2017 |
| Fractional Feynman-Kac equation for non-Brownian functionals L Turgeman, S Carmi, E Barkai Physical review letters 103 (19), 190201, 2009 | 101 | 2009 |
| On distributions of functionals of anomalous diffusion paths S Carmi, L Turgeman, E Barkai Journal of Statistical Physics 141, 1071-1092, 2010 | 95 | 2010 |
| A mixed-ensemble model for hospital readmission L Turgeman, JH May Artificial intelligence in medicine 72, 72-82, 2016 | 93 | 2016 |
| Photon efficiency optimization in time-correlated single photon counting technique for fluorescence lifetime imaging systems L Turgeman, D Fixler IEEE Transactions on Biomedical Engineering 60 (6), 1571-1579, 2013 | 50 | 2013 |
| Proceedings from the 9th annual conference on the science of dissemination and implementation: Washington, DC, USA. 14-15 December 2016 D Chambers, L Simpson, G Neta, UT Schwarz, A Percy-Laurry, ... Implementation Science 12, 1-55, 2017 | 18 | 2017 |
| Time-averaged fluorescence intensity analysis in fluorescence fluctuation polarization sensitive experiments L Turgeman, D Fixler Biomedical optics express 4 (6), 868-884, 2013 | 16 | 2013 |
| Unsupervised learning approach to estimating user engagement with mobile applications: A case study of The Weather Company (IBM) L Turgeman, O Smart, N Guy Expert systems with applications 120, 397-412, 2019 | 15 | 2019 |
| Reference‐independent wide field fluorescence lifetime measurements using Frequency‐Domain (FD) technique based on phase and amplitude crossing point G Yahav, E Barnoy, N Roth, L Turgeman, D Fixler Journal of biophotonics 10 (9), 1198-1207, 2017 | 12 | 2017 |
| The influence of dead time related distortions on live cell fluorescence lifetime imaging (FLIM) experiments L Turgeman, D Fixler Journal of Biophotonics 7 (6), 442-452, 2014 | 11 | 2014 |
| Identification of readmission risk factors by analyzing the hospital-related state transitions of congestive heart failure (CHF) patients L Turgeman, J May, A Ketterer, R Sciulli, D Vargas IIE Transactions on Healthcare Systems Engineering 5 (4), 255-267, 2015 | 10 | 2015 |
| System and method for measuring user engagement I Ben-Harrush, N Ifergan-Guy, L Turgeman US Patent 10,834,213, 2020 | 9 | 2020 |
| Short time behavior of fluorescence intensity fluctuations in single molecule polarization sensitive experiments L Turgeman, D Fixler Optics Express 20 (8), 9276-9283, 2012 | 9 | 2012 |
| Context-aware incremental clustering of alerts in monitoring systems L Turgeman, Y Avrashi, G Vagner, N Azaizah, S Katkar Expert Systems with Applications 210, 118489, 2022 | 7 | 2022 |
| Adaptation of deep learning models to resource constrained edge devices L Turgeman, N Naaman, M Masin, N Guy, S Kalner, I Rosen, A Amir US Patent 11,928,583, 2024 | 6 | 2024 |
| Controlling performance of deployed deep learning models on resource constrained edge device via predictive models L Turgeman US Patent 12,223,419, 2025 | 3 | 2025 |
| A time-dependent principal components-based dimension reduction approach to analyzing the influence of product interventions on user engagement with mobile applications L Turgeman, O Smart 2018 IEEE World Congress on Services (SERVICES), 11-12, 2018 | 3 | 2018 |
| Whole-Object Fluorescence Lifetime Setup for Efficient Non-Imaging Quantitative Intracellular Fluorophore Measurements Y Namer, L Turgeman, M Deutsch, D Fixler Journal of fluorescence 22, 875-882, 2012 | 1 | 2012 |
| From non-Brownian Functionals to a Fractional Schr\" odinger Equation L Turgeman, S Carmi, E Barkai arXiv preprint arXiv:0909.0144, 2009 | 1 | 2009 |
| Modifying artificial intelligence models using model fragments N Naaman, I Rosen, L Turgeman, N Guy, S Kallner, A Amir US Patent 11,829,888, 2023 | | 2023 |