Over the last few decades, machine learning and data mining have been increasingly used for clini... more Over the last few decades, machine learning and data mining have been increasingly used for clinical prediction in ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers such as logistic regression and support vector machines, our model not only incorporates the discriminative features derived from the time-series, but also naturally exploits the temporal orders of these features based on a Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV, which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs.
Over the last few decades, machine learning and data mining have been increasingly used for clini... more Over the last few decades, machine learning and data mining have been increasingly used for clinical prediction in ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers such as logistic regression and support vector machines, our model not only incorporates the discriminative features derived from the time-series, but also naturally exploits the temporal orders of these features based on a Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV, which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs.
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Papers by Marin Kollef
ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at
filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the
classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers
such as logistic regression and support vector machines, our model not only incorporates the discriminative
features derived from the time-series, but also naturally exploits the temporal orders of these features based on a
Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our
TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV,
which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs.
ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at
filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the
classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers
such as logistic regression and support vector machines, our model not only incorporates the discriminative
features derived from the time-series, but also naturally exploits the temporal orders of these features based on a
Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our
TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV,
which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs.