Early detection of sepsis utilizing deep learning on electronic health record event sequences

Abstract

Background: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units. Furthermore, we will circumvent several important limitations that have been found in the literature: 1) Models are evaluated shortly before sepsis onset without considering interventions already initiated. 2) Machine learning models are built on a restricted set of clinical parameters, which are not necessarily measured in all departments. 3) Model performance is limited by current knowledge of sepsis, as feature interactions and time dependencies are hard-coded into the model. Methods: In this study, we present a model to overcome these shortcomings using a deep learning approach on a diverse multicenter data set. We used retrospective data from multiple Danish hospitals over a seven-year period. Our sepsis detection system is constructed as a combination of a convolutional neural network and a long short-term memory network. We suggest a retrospective assessment of interventions by looking at intravenous antibiotics and blood cultures preceding the prediction time. Results: Results show performance ranging from AUROC 0.856 (3 hours before sepsis onset) to AUROC 0.756 (24 hours before sepsis onset). Evaluating the clinical utility of the model, we find that a large proportion of septic patients did not receive antibiotic treatment or blood culture at the time of the sepsis prediction, and the model could therefore facilitate such interventions at an earlier point in time. Conclusion: We present a deep learning system for early detection of sepsis that is able to learn characteristics of the key factors and interactions from the raw event sequence data itself, without relying on a labor-intensive feature extraction work. Our system outperforms baseline models, such as gradient boosting, which rely on specific data elements and therefore suffer from many missing values in our dataset.

Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review

“…The results of the literature search, including the numbers of studies screened, assessments for eligibility, and articles reviewed (with reasons for exclusions at each stage), are presented in Figure 1. Out of 974 studies, 22 studies met the inclusion criteria [Abromavičius et al, 2020, Barton et al, 2019, Bloch et al, 2019, Desautels et al, 2016, Futoma et al, 2017b, Kaji et al, 2019, Kam and Kim, 2017, Lauritsen et al, 2020, Lukaszewski et al, 2008, Mao et al, 2018, McCoy and Das, 2017, Moor et al, 2019, Nemati et al, 2018, Reyna et al, 2019…”

“…Depending on the definition of sepsis used, and whether subsampling of controls was used to achieve a more balanced class ratio (facilitating the training of machine learning models), the prevalence of patients developing sepsis ranged between 6.2% and 63.6% ( Figure 2). One study did not report the prevalence [Lauritsen et al, 2020]. Concerning demographics, 9 studies reported the median or mean age, 12 the prevalence of female patients, and solely 1 the ethnicity of the investigated cohorts (Supplementary Table 4).…”

“…The number of variables included in the respective models ranged between 2 [Shashikumar et al, 2017a] and 119 [Kaji et al, 2019]. While reporting the type of variables, four studies failed to report the number of variables included in the models [Lauritsen et al, 2020, Lukaszewski et al, 2008, McCoy and Das, 2017, Sheetrit et al, 2019.…”

Early Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs

“…al. [4] proposes a deep learning-based early detection method which learns features by itself from clinical time-series data. It overcomes the shortcomings of machine learning using a deep learning approach on a diverse multicenter dataset.…”

Explainable artificial intelligence model to predict acute critical illness from electronic health records

“…rtificial Intelligence (AI) is capable of predicting acute critical illness earlier and with greater accuracy than traditional early warning score (EWS) systems, such as modified EWSs (MEWSs) and sequential organ failure assessment scores (SOFAs) [1][2][3][4][5][6][7][8][9][10][11][12][13] . Unfortunately, standard deep learning (DL) that comprise available AI models are black-box predictions that cannot readily be explained to clinicians.…”