| Using supervised machine learning to classify real alerts and artifact in online multi-signal vital sign monitoring data L Chen, A Dubrawski, D Wang, M Fiterau, M Guillame-Bert, E Bose, ... Critical care medicine 44 (7), e456, 2016 | 62 | 2016 |
| Learning temporal association rules on symbolic time sequences M Guillame-Bert, JL Crowley Asian conference on machine learning, 159-174, 2012 | 39 | 2012 |
| Learning temporal rules to forecast instability in continuously monitored patients M Guillame-Bert, A Dubrawski, D Wang, M Hravnak, G Clermont, ... Journal of the American Medical Informatics Association 24 (1), 47-53, 2017 | 20 | 2017 |
| Classification of time sequences using graphs of temporal constraints M Guillame-Bert, A Dubrawski The Journal of Machine Learning Research 18 (1), 4370-4403, 2017 | 19 | 2017 |
| Data-driven classification of screwdriving operations RM Aronson, A Bhatia, Z Jia, M Guillame-Bert, D Bourne, A Dubrawski, ... International Symposium on Experimental Robotics, 244-253, 2016 | 16 | 2016 |
| Predicting home service demands from appliance usage data K Basu, M Guillame-Bert, H Joumaa, S Ploix, J Crowley International conference on information and communication technologies and …, 2011 | 15 | 2011 |
| New approach on temporal data mining for symbolic time sequences: Temporal tree associate rules M Guillame-Bert, JL Crowley 2011 IEEE 23rd International Conference on Tools with Artificial …, 2011 | 12 | 2011 |
| First-order logic learning in artificial neural networks M Guillame-Bert, K Broda, AA Garcez The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010 | 11 | 2010 |
| Exact distributed training: Random forest with billions of examples M Guillame-Bert, O Teytaud arXiv preprint arXiv:1804.06755, 2018 | 7 | 2018 |
| Learning temporal rules to forecast events in multivariate time sequences M Guillame-Bert, A Dubrawski 2nd Workshop on Machine Learning for Clinical Data Analysis, Healthcare and …, 2014 | 7 | 2014 |
| Increasing cardiovascular data sampling frequency and referencing it to baseline improve hemorrhage detection A Wertz, AL Holder, M Guillame-Bert, G Clermont, A Dubrawski, ... Critical care explorations 1 (10), 2019 | 6 | 2019 |
| Artifact patterns in continuous noninvasive monitoring of patients M Hravnak, L Chen, E Bose, M Fiterau, M Guillame-Bert, A Dubrawski, ... Intensive care medicine 39 (Suppl 2), S405, 2013 | 5 | 2013 |
| Utility of empirical models of hemorrhage in detecting and quantifying bleeding M Guillame-Bert, A Dubrawski, L Chen, A Holder, MR Pinsky, G Clermont Intensive Care Medicine 40, S287-S287, 2014 | 3 | 2014 |
| Learning temporal rules to forecast instability in intensive care patients. M Guillame-Bert, A Dubrawski, L Chen, M Hravnak, M Pinsky, G Clermont Intensive care medicine 39 (Suppl 2), S470-S470, 2013 | 3 | 2013 |
| Planning with inaccurate temporal rules M Guillame-Bert, JL Crowley 2012 IEEE 24th International Conference on Tools with Artificial …, 2012 | 3 | 2012 |
| Detection of hemorrhage by analyzing shapes of the arterial blood pressure waveforms SR Zambrano, M Guillame-Bert, A Dubrawski, G Clermont, MR Pinsky Intensive Care Medicine Experimental 3 (1), 1-2, 2015 | 2 | 2015 |
| Modeling Text with Decision Forests using Categorical-Set Splits M Guillame-Bert, S Bruch, P Mitrichev, P Mikheev, J Pfeifer arXiv preprint arXiv:2009.09991, 2020 | 1 | 2020 |
| Batched Lazy Decision Trees M Guillame-Bert, A Dubrawski arXiv preprint arXiv:1603.02578, 2016 | 1 | 2016 |
| Semi automated adjudication of vital sign alerts in step-down units M Fiterau, A Dubrawski, D Wang, L Chen, M Guillame-Bert, M Hravnak, ... Intensive Care Medicine Experimental 3 (1), 1-2, 2015 | 1 | 2015 |
| Supervised Machine Learning Can Classify Artifact In Multi-Signal Vital Sign Monitoring Data Erom Step-Down Unit (SDU) Patients M Hravnak, L Chen, A Dubrawski, G Clermont, E Bose, M Fiterau, D Wang, ... Intensive Care Medicine 40, S28-S28, 2014 | 1 | 2014 |
