Septic Shock Diagnosis by Neural Networks and Rule Based Systems

Brause, Rüdiger; Hamker, Fred H.; Paetz, Jürgen

doi:10.1007/978-3-7908-1788-1_12

Cited by 25 publications

(16 citation statements)

References 20 publications

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“…It is important to note that we designed the study as a classification task rather than a time-to-event modeling experiment, because the former is significantly more common in the literature [28][29][30][31]. The alternative would not allow for the use of an established, standard set of performance metrics such as AUROC and specificity without custom modification, and would make it more difficult to compare the present study to prior work in the field.…”

Section: Limitationsmentioning

confidence: 99%

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Mao

Jay

Hoffman

et al. 2018

Preprint

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Objectives: We validate a machine learning-based sepsis prediction algorithm (InSight) for detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings. Design:A machine learning algorithm with gradient tree boosting. Features for prediction were created from combinations of only six vital sign measurements and their changes over time. Conclusions:InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions. Strengths and limitations of this study •Machine learning is applied to the detection and prediction of three separate sepsis standards in the emergency department, general ward and intensive care settings.

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Section: Limitationsmentioning

confidence: 99%

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Mao

Jay

Hoffman

et al. 2018

Preprint

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“…In the retrospective analysis, we treated severe sepsis detection and prediction as a classification task. While a time-to-event modeling approach would have also been possible, classification methods are significantly more common in the literature [19,[27][28][29][30]. By using the same modelling approach, the present study can be readily compared with existing work on sepsis detection models using standard metrics such as AUROC and specificity.…”

Section: Limitationsmentioning

confidence: 99%

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

Burdick

Pino

Gabel-Comeau

et al. 2018

Preprint

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KEY POINTSQuestion: Is a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?Findings: In a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset . In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate. Meaning:The accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes. ABSTRACTObjective: To validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.Setting: This cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.Participants: For retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included. Results:At severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P <.001), SOFA (0.74; P <.001), and SIRS (0.62; P <.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate. Conclusions:The MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.

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“…A diagnostic system for Septic Shock based on ANNs (Radial Basis Functions -RBF-and supervised Growing Neural Gas) was presented in [50], reporting an overall correct classification rate of 67.84%, with a high specificity of 91.61%, but an extremely poor sensitivity of 24.94%. Also in this area, Brause et al [51] applied an evolutionary algorithm to an RBF network (the MEDAN Project) to obtain, over a retrospective dataset, a set of predictive attributes for assessing mortality for Abdominal Sepsis, namely Systolic and Diastolic blood pressure and thrombocytes.…”

Section: Quantitative Analysis Of the Prognosis Of Sepsismentioning

confidence: 99%

Intelligent Management of Sepsis in the Intensive Care Unit

Ripoll

Rodríguez

Vellido

2012

Medical Applications of Intelligent Data Analysis

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Sepsis is a transversal pathology and one of the main causes of death in the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 60% for Septic Shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by Sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief contribution, three different methods are presented. One is based on a variant of the well-known support vector machine (SVM) model and provides and automated ranking of relevance of the mortality predictors while the other two are based on logistic-regression and logistic regression over latent Factors. The reported results show that the methods presented outperform in terms of accuracy alternative techniques currently in use in clinical settings, while simultaneously assessing the relative impact of individual pathology indicators.

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Septic Shock Diagnosis by Neural Networks and Rule Based Systems

Cited by 25 publications

References 20 publications

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

Intelligent Management of Sepsis in the Intensive Care Unit

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