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Dive into the research topics where Mathieu Guillame-Bert is active.

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Featured researches published by Mathieu Guillame-Bert.


Critical Care Medicine | 2016

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Lujie Chen; Artur Dubrawski; Donghan Wang; Madalina Fiterau; Mathieu Guillame-Bert; Eliezer Bose; Ata Murat Kaynar; David J. Wallace; Jane Guttendorf; Gilles Clermont; Michael R. Pinsky; Marilyn Hravnak

Objective: The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Design: Observational cohort study. Setting: Twenty-four–bed trauma step-down unit. Patients: Two thousand one hundred fifty-three patients. Intervention: Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. Measurements and Main Results: The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67–0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71–0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64–0.95) and increased to 0.87 (95% CI, 0.71–0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77–0.95) and increased to 0.97 (95% CI, 0.94–1.00). Heart rate alerts were too few for model development. Conclusions: Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).


Journal of the American Medical Informatics Association | 2017

Learning temporal rules to forecast instability in continuously monitored patients

Mathieu Guillame-Bert; Artur Dubrawski; Donghan Wang; Marilyn Hravnak; Gilles Clermont; Michael R. Pinsky

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event.


Intensive Care Medicine | 2013

LEARNING TEMPORAL RULES TO FORECAST INSTABILITY IN INTENSIVE CARE PATIENTS.

Mathieu Guillame-Bert; Artur Dubrawski; Lujie Chen; Marilyn Hravnak; Michael R. Pinsky; Gilles Clermont

ESICM LIVES 2013 26th Annual Congress Paris, France 5–9 October This supplement issue of the official ESICM/ESPNIC journal Intensive Care Medicine contains abstracts of scientific papers presented at the 26th Annual Congress of the European Society of Intensive Care Medicine. The abstracts appear in order of presentation from Monday 7 October to Wednesday 9 October 2013. The same abstract numbering is used in the Congress Final Programme. This supplement was not sponsored by outside commercial interests; it was funded entirely by the society’s own resources. DOI:10.1007/s00134-013-3095-5 123 26th ANNUAL CONGRESS—PARIS, FRANCE—5–9 OCTOBER 2013 26th ANNUAL CONGRESS—PARIS, FRANCE—5–9 OCTOBER 2013


international symposium on experimental robotics | 2016

Data-Driven Classification of Screwdriving Operations.

Reuben M. Aronson; Ankit Bhatia; Zhenzhong Jia; Mathieu Guillame-Bert; David Alan Bourne; Artur Dubrawski; Matthew T. Mason

Consumer electronic devices are made by the millions, and automating their production is a key manufacturing challenge. Fastening machine screws is among the most difficult components of this challenge. To accomplish this task with sufficient robustness for industry, detecting and recovering from failure is essential. We have built a robotic screwdriving system to collect data on this process. Using it, we collected data on 1862 screwdriving runs, each consisting of force, torque, motor current and speed, and video. Each run is also hand-labeled with the stages of screwdriving and the result of the run. We identify several distinct stages through which the system transitions and relate sequences of stages to characteristic failure modes. In addition, we explore several techniques for automatic result classification, including standard maximum angle/torque methods and machine learning time series techniques.


Intensive Care Medicine Experimental | 2015

Detection of hemorrhage by analyzing shapes of the arterial blood pressure waveforms

S Romero Zambrano; Mathieu Guillame-Bert; Artur Dubrawski; Gilles Clermont; Pinsky

We hypothesize that changes of shape of arterial blood pressure (ABP) high-frequency waveform signal can be reflective of bodys response to stress, in particular to hemorrhage.


Intensive Care Medicine Experimental | 2015

Semi automated adjudication of vital sign alerts in step-down units

Madalina Fiterau; Artur Dubrawski; Donghan Wang; Lujie Chen; Mathieu Guillame-Bert; Marilyn Hravnak; Gilles Clermont; Eliezer Bose; Andre Holder; A. Murat Kaynar; David J. Wallace; Pinsky

Machine Learning (ML) has shown predictive utility in analyzing vital sign (VS) data collected from physiologically unstable monitored patients. Training an ML model usually requires sizable amounts of labeled ground-truth data typically obtained via laborious manual chart reviews by expert clinicians.


Intensive Care Medicine Experimental | 2015

Forecasting escalation of cardio-respiratory instability using noninvasive vital sign monitoring data

Mathieu Guillame-Bert; Artur Dubrawski; Lujie Chen; Marilyn Hravnak; Gilles Clermont; Pinsky

Critical cardio-respiratory instability (CRIcrit) poses a substantial risk for patients in step-down-units (SDU) and requires immediate medical attention. It is often preceded by mild episodes of CRI (CRImild).


Journal of Machine Learning Research | 2017

Classification of Time Sequences using Graphs of Temporal Constraints

Mathieu Guillame-Bert; Artur Dubrawski


arXiv: Learning | 2016

Batched Lazy Decision Trees.

Mathieu Guillame-Bert; Artur Dubrawski


Intensive Care Medicine | 2013

Artifact Patterns in Continuous Noninvasive Monitoring of Patients.

Marilyn Hravnak; Lujie Chen; Eliezer Bose; Madalina Fiterau; Mathieu Guillame-Bert; Artur Dubrawski; Gilles Clermont; Michael R. Pinsky

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Artur Dubrawski

Carnegie Mellon University

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Lujie Chen

Carnegie Mellon University

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Andre Holder

University of Pittsburgh

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Donghan Wang

Carnegie Mellon University

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Eliezer Bose

University of Pittsburgh

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Madalina Fiterau

Carnegie Mellon University

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Pinsky

University of Pittsburgh

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