Mathieu Guillame-Bert
Mathieu Guillame-Bert
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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
Learning temporal association rules on symbolic time sequences
M Guillame-Bert, JL Crowley
Asian conference on machine learning, 159-174, 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
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
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
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
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
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
Exact distributed training: Random forest with billions of examples
M Guillame-Bert, O Teytaud
arXiv preprint arXiv:1804.06755, 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
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
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
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
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
Planning with inaccurate temporal rules
M Guillame-Bert, JL Crowley
2012 IEEE 24th International Conference on Tools with Artificial …, 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
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
Batched Lazy Decision Trees
M Guillame-Bert, A Dubrawski
arXiv preprint arXiv:1603.02578, 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
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
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