Sequential Deep Learning Models for Neonatal Sepsis Detection : A suitability assessment of deep learning models for event detection in physiological data

Sammanfattning: Sepsis is a life-threatening condition that neonatal patients are especially susceptible to. Fortunately, improved bedside monitoring has enabled the collection and use of continuous vital signs data for the purpose of detecting conditions such as sepsis. While current research has found some success in reducing mortality in neonatal intensive care units with linear directly interpretable models, such as logistic regression, accurate detection of sepsis from inherently noisy time-series data still remains a challenge. Furthermore, previous research has generally relied on pre-defined features extracted from rawvital signs data, which may not be optimal for the detection task. Therefore, assessing the overall feasibility of sequential deep learning models, such as recurrent and convolutional models, could improve the results of current research. This task was tackled in three phases. Firstly, baseline scores were established with a logistic regression model. Secondly, three common recurrent classifiers were tested on pre-defined window based features and compared with each other. Thirdly, a convolutional architecture with a recurrent and non-recurrent classifier was tested on raw low frequency (1Hz) signals in order to examine their capability to automatically extract features from the data. The final results from all phases were compared with each other. Results show that recurrent classifiers trained on pre-defined features do outperform automatic feature extraction with the convolutional models. The best model was based on a long-short term memory unit that achieved an area under the characteristic receiver operating unit curve of 0.806, and outperformed the established baseline results. In comparison with previous research, said model performed on par with the examined simple interpretable baseline models. The low results can likely be attributed to a insufficient sample size of patients with sepsis for the examined models and sub-optimal hyperparameter optimization due to the number of possible configurations. Further avenues of research include examination of high frequency data and more complex models for automatic feature extraction.