Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction

Abstract

Objective Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs. Methodology We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay. These features are used in machine learning algorithms to provide a prediction of a patient's likelihood to develop sepsis during ICU stay, hours before it is diagnosed. Results Five machine learning algorithms were implemented using R software packages. The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs. These algorithms aimed to calculate a patient's probability to become septic within the next 4 hours, based on recordings from the last 8 hours. The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%. Conclusions The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs. Variability of a patient's vital signs proves to be a good indicator of one's chance to become septic during ICU stay.

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, Calvert et al, 2016, 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, Schamoni et al, 2019, Scherpf et al, 2019, Shashikumar et al, 2017a,b, Sheetrit et al, 2019, Van Wyk et al, 2019, van Wyk et al, 2019]. The majority of excluded studies ( n = 952) did not meet one or multiple inclusion criteria, such as studying a non-human (e.g.…”

“…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…”

A Deep Learning-Based Sepsis Estimation Scheme

“…As specified in similar research [10], the gradient boosting model has every tree split for a maximum of six times, while tree aggregation is limited to a thousand trees for predicting the risk values. As clear insight to the results obtained via several running (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) level of training we found that the sepsis features can be detected easily from the clinical datasets provided in the context of standardised feature values which can be tuned to be in the range of (0-300 iteration) for 17 feature in the range of (0-2000 iteration) for 34 feature regime as shown in figures 8 and 9 respectively. These results come with different projection on the main feature set classification, as part of recursive training paradigm used in the pre-processing stage.…”

“…These systems could be trained using individual Electronic Health Records (EHRs) [14]- [15][16] [17]. It has been found the ML-based sepsis prediction models have significantly higher predictive capabilities compared to the score-based early warning systems like the National Early Warning System (NEWS) [18]- [19] [20]. Shimabukuro et al, illustrated numerous advantages of using an ML-based classification system for sepsis detection.…”

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

“…Sepsis and machine learning. More recently machine learning has become the major player in the predictive analysis of sepsis data, leading to a massive wave of studies targeting different aspects of the problem, from the general issue [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] , to more specific objectives or methods. For instance, many studies have been defining, combining and validating score risks 52,53 , predicting early onset [54][55][56] , or focusing on pediatric aspects 57 or on the immediate applicability to clinical practice [58][59][60] .…”