Real-time prediction of an anesthetic monitor index using machine learning. Caelen, O., Cailloux, O., Ghoundiwal, D., Miranda, A. A., Barvais, L., & Bontempi, G. In Proceedings of The First International Workshop on Knowledge Discovery in Health Care and Medicine, pages 78 – 89, Athens, Greece, September, 2011. abstract bibtex An anesthesiologist controls the level of consciousness of a patient undergoing surgery by appropriately dosing hypnotic drugs. The information provided by the monitoring devices may be utilized in order to accomplish this task. One such monitor provides a dimensionless quantity derived from the electroencephalogram called bispectral index (BIS), which could quantify the level of awareness of the patient. This article discusses the use of machine learning techniques to implement a predictive model of the BIS based on the variation of the hypnotic drugs. Such a model learned from a database of recorded operations can aid realtime decision making during the course of an operation. In order to deal with inter-individual variability, the proposed model takes into account patient physiology as well as the reactions of the patient during the early phases of the operation. Two models of the bispectral index behavior are assessed and compared in this work: a linear predictor and a local learning predictor. These prediction models were software implemented and their accuracies were assessed by a computerized cross-validation study and were tested in real situations.
@inproceedings{caelen_real-time_2011,
address = {Athens, Greece},
title = {Real-time prediction of an anesthetic monitor index using machine learning},
abstract = {An anesthesiologist controls the level of consciousness of a patient undergoing surgery by appropriately dosing hypnotic drugs. The information provided by the monitoring devices may be utilized in order to accomplish this task. One such monitor provides a dimensionless quantity derived from the electroencephalogram called bispectral index (BIS), which could quantify the level of awareness of the patient. This article discusses the use of machine learning techniques to implement a predictive model of the BIS based on the variation of the hypnotic drugs. Such a model learned from a database of recorded operations can aid realtime decision making during the course of an operation.
In order to deal with inter-individual variability, the proposed model takes into account patient physiology as well as the reactions of the patient during the early phases of the operation. Two models of the bispectral index behavior are assessed and compared in this work: a linear predictor and a local learning predictor. These prediction models were software implemented and their accuracies were assessed by a computerized cross-validation study and were tested in real situations.},
booktitle = {Proceedings of {The} {First} {International} {Workshop} on {Knowledge} {Discovery} in {Health} {Care} and {Medicine}},
author = {Caelen, Olivier and Cailloux, Olivier and Ghoundiwal, Djamal and Miranda, Abhilash Alexander and Barvais, Luc and Bontempi, Gianluca},
month = sep,
year = {2011},
pages = {78 -- 89}
}
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