Deschrijver, Dirk

Dhaene, Tom

Maes, Aren

Van Steenkiste, Tom

English

Ghent University Academic Bibliography

A study of early sepsis detection models basedon multivariate medical time seriesAren Maes[0000−0003−1637−953X], Tom Van Steenkiste[0000−0002−3842−3151],Tom Dhaene[0000−0003−2899−4636], and Dirk Deschrijver[0000−0001−6600−1792]Ghent University - imec, IDLab, Technologiepark-Zwijnaarde 126, 9052 Ghent,Belgium aren.maes@ugent.be - www.ugent.be/ea/idlab/enAbstract. Sepsis is a life-threatening complication caused by the body’sresponse to an infection. For that reason, it is important to have an accu-rate method to detect sepsis as early as possible. The features extractedfrom the used ICU data have missing values and non-uniform samplingfrequencies, hence an advanced GP based interpolation method is pro-posed that increases the performance of the models. Additionally, thisthesis abstract develops and compares different sepsis detection modelsbased on real medical data [1]. The results show that accurate modelscan be developed to predict the occurrence of sepsis during an ICU stay.Keywords: Sepsis · Early detection model · Medical time series · GP.1 IntroductionWorldwide, more than 30 million people are affected by sepsis each year, of which6 million people die as the mortality rate lies between 17% and 26% [2]. Hence, itis important to detect sepsis as early as possible. Current tests to diagnose sepsisare time consuming and often inaccurate. Therefore, an automatic detectionsystem could be beneficial. In this work, electronic measurements of the patient’scurrent condition are used to develop a machine learning model that can predictsepsis, allowing doctors to start a treatment as early as possible.2 Prediction modelsThis work makes use of the MIMIC-III database from which 48063 samples witheach 34 features are extracted. A 70 -10 -20 % ratio was used for the train, val-idation and test sets. The features are based on existing sepsis scores and usea combination of vital signs and laboratory results. To address missing valuesand different sampling frequencies of these features, a Gaussian Process basedinterpolation technique is introduced that fits the data. The interpolated pointsconsist of a value and its uncertainty which is used to improve the models. Exper-imental results confirm the superiority of this technique to linear interpolation(see Figure 1).Copyright c© 2019 for this paper by its authors. Use permitted under CreativeCommons License Attribution 4.0 International (CC BY 4.0).2 A. Maes et al.Secondly, the performance of different models are compared to each other.The first three models (L1D, L3D and L5D) consist of one up to five LSTMlayers, followed by a fully connected layer as the final layer of the model. Thenext three models (CL1D, CL3D and C2L3D) are similar to the previous onesexcept that they are now preceded by one or more 1D convolutional layers.These combined architectures take advantage of the automatic feature extractionproperty of CNNs and improve the model results.3 ResultsFigure 2 shows the performance of these models using the area under ROCmetric. Comparing the L1D, L3D and L5D models to each other, a small im-provement can be noted. The models with the extra convolutional layers performbetter than without these extra layers but exceptions exists.L1D L3D L5D CL1D C2L3D0.00.20.40.60.81.0Area under ROCLinear interpolationGP interpolationFig. 1. Area under ROC performance oflinear and GP interpolation.L1D L3D L5D CL1D CL3D C2L3D0.00.20.40.60.81.0Area under ROC0.789 0.825 0.835 0.814 0.817 0.835Fig. 2. Area under ROC performance ofdifferent models, tested on MIMIC-III.4 ConclusionIn this work, an advanced interpolation method based on Gaussian processes wastested and compared to linear interpolation. The experiments showed that theGP interpolation method is superior to such a simpler method. Secondly, a com-bined architecture, called convolutional recurrent neural network, was developed.Experimental results showed that this architecture performs better in generalthan a regular RNN or CNN. Such architecture, with additional optimisationslike hyperparameter tuning and dropout, can obtain an area under ROC of 0,918.References1. A. Maes, “Human health monitoring using machine learning and data analysis,”Master’s thesis, Ghent University, 2019. Via https://lib.ugent.be/en/catalog/rug01:002785831?i=0&q=Human+health+monitoring+using+machine+learning+and+data+analysis.2. C. Fleischmann, A. Scherag, N. K. Adhikari, C. S. Hartog, T. Tsaganos,P. Schlattmann, D. C. Angus, and K. Reinhart, “Assessment of global incidenceand mortality of hospital-treated sepsis. current estimates and limitations,” Amer-ican journal of respiratory and critical care medicine, vol. 193, no. 3, pp. 259–272,2016.

A study of early sepsis detection models based on multivariate medical time series

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A study of early sepsis detection models based on multivariate medical time series

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