Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models

Abromavičius, Vytautas; Plonis, Darius; Tarasevičius, Deividas; Serackis, Artūras

doi:10.3390/electronics9071133

Cited by 21 publications

(9 citation statements)

References 42 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…It is important to note that some studies modified the Sepsis-2 or Sepsis-3 definition since all existing definitions have not been intended to specify an exact sepsis onset time (e.g. the employed time window lengths have been varied) [Abromavičius et al, 2020, Nemati et al, 2018]. In one study [Schamoni et al, 2019], sepsis labels were assigned by trained ICU experts.…”

Section: Resultsmentioning

confidence: 99%

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

Moor

Rieck

Horn

et al. 2020

Preprint

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Background: Sepsis is among the leading causes of death in intensive care units (ICU) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study eligibility criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study appraisal and synthesis methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from "poor" (satisfying less than 40% of the quality criteria) to "very good" (satisfying more than 90% of the quality criteria). The majority of the studies (n= 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n= 2,9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only 2 studies provided publicly-accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and key findings: A growing number of studies employs machine learning to31optimise the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic review registration number: CRD42020200133

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Moor

Rieck

Horn

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In terms of sepsis definition, the majority of the studies employed the Sepsis-2 (n = 12; 54.5%) or Sepsis-3 definition (n = 9; 40.9%). It is important to note that some studies modified the Sepsis-2 or Sepsis-3 definition since all existing definitions have not been intended to specify an exact sepsis onset time (e.g., the employed time window lengths have been varied) (26,34). In one study (36), sepsis labels were assigned by trained ICU experts.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…This amounts to < 10% of the eligible studies. In addition, only four studies (18,21,26,33) report results on publicly available datasets (more precisely, the datasets are available for research after accepting their terms and conditions). This finding is surprising, given the existence of high-quality, freely accessible databases, such as MIMIC-III (13) or eICU (63).…”

Section: Reproducibilitymentioning

confidence: 99%

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

et al. 2021

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Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis.Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU.Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU.Study Eligibility Criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded.Study Appraisal and Synthesis Methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed.Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from “poor” (satisfying ≤ 40% of the quality criteria) to “very good” (satisfying ≥ 90% of the quality criteria). The majority of the studies (n = 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n = 2, 9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only two studies provided publicly accessible source code and data sources fostering reproducibility.Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU.Conclusions and Key Findings: A growing number of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses.Systematic Review Registration Number: CRD42020200133.

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“…The deep features were taken from the first layers output using . Also, the confusion matrices (TP, TN, FP, FN) were calculated to assess the effectiveness of the classifier by giving the same weighting to each of the four groups [48][49][50].…”

Section: Parameters Tuningmentioning

confidence: 99%

A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision

Archila¹,

Manzanera

Martínez³

2022

Computer Methods and Programs in Biomedicine

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Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models

Cited by 21 publications

References 42 publications

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

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

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

A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision

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