Detection of clinically important colorectal surgical site infection using Bayesian network

Sohn, Sunghwan; Larson, David W.; Habermann, Elizabeth B.; Naessens, James M.; Alabbad, Jasim; Liu, Hongfang

doi:10.1016/j.jss.2016.09.058

Cited by 51 publications

(45 citation statements)

References 20 publications

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“…BNs have been extensively used in different areas of research such as in the chemical mode of action classification for aquatic toxicology (Carriger et al, 2016) and ecological risk assessment (Lee & Lee, 2006;Pollino et al, 2007); to classify images in medical image analysis (Arias et al, 2016); to predict food fraud ; to detect surgical site infections and safety assessment of natural gas stations (Sohn et al, 2016;Zarei et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

et al. 2017

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In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal-and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

et al. 2017

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“…There were 21 single-center studies 33,70-75, [77][78][79][80][81][82][83][86][87][88][90][91][92]94 and six multi-center studies 35,76,84,85,89,93 . Seven studies were time series 71,78,82,[84][85][86]92 , 18 studies were case series 33,35,70, [72][73][74][75][76]80,81,83,[87][88][89][90][91]93,94 , one was a case-control 77 and one was a matched-controlled study 79 .…”

Section: Study Characteristicsmentioning

confidence: 99%

“…The smallest studies included patients with leukemia 89 and combat casualty patients 90 . Four studies had a sample size below 1000 70, 72,73,79 , three had a sample size between 1001-10,000 33, 71,87 and 12 had a sample size larger than 10,000 35,74,77-78,80-82,84-87,88 . Eight studies had samples even larger than 50,000 35,74,77,78,82,84,85,88 .…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Majority of the published studies evaluated data from different wards; several studies included patients admitted only to the ICU 70, 72,81,[84][85][86]93 and surgical ward 73,76,78,87,91,92 , less often the General ward 33 and Emergency Department 74 . Of these, 23 studies included data collected at their own hospital; and four utilized previously collated databases 76,81,84,86 .…”

Section: Study Characteristicsmentioning

confidence: 99%

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Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Medic

Atallah

et al. 2019

F1000Res

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Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

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“…BNs have been extensively used in different areas of research such as in the chemical mode of action classification for aquatic toxicology (Carriger et al, 2016) and ecological risk assessment (Lee and Lee, 2006;Pollino et al, 2007); to classify images in medical image analysis (Arias et al, 2016); to predict food fraud (Bouzembrak and Marvin, 2016;Marvin et al, 2016a); to assess risk of nanomaterials (Money et al, 2014;Winkler et al, 2014;Linkov et al, 2015;Low-Kam et al, 2015); to detect surgical site infections and safety assessment of natural gas stations (Sohn et al, 2017;Zarei et al, 2017).…”

Section: Disclaimermentioning

confidence: 99%

Applicability of a food chain analysis on aquaculture of Atlantic salmon to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks

Marvin

Bouzembrak

Asselt

et al. 2019

EFSA Supporting Publications

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The objective of Aquarius project was to test the applicability of a food chain analysis on Atlantic salmon farmed in Norway to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks. To this end, a comprehensive literature review was conducted complemented with expert views collected by on‐line questionnaires and in‐depth interviews. This review provided an overview of the various stages in the supply chain from farm to consumer and the identification of i) existing and emerging human and animal health hazards, ii) vulnerabilities, iii) control measures, iv) drivers of change, and v) related indicators. Next step in the study was to i) complement the list of vulnerabilities followed by a prioritisation using focus group discussions and Failure Mode and Effect Analysis (FMEA) method and ii) link drivers of change acting upon the (prioritised) vulnerabilities and identification of associated indicators and data sources by means of a Delphi method. Based on the experiences obtained with the different methodologies during this project, the Aquarius team considers literature review, and a direct face‐to‐face interaction with experts (in‐depth interviews, focus group discussions and FMEA in a workshop setting) as being most effective regarding output and effort. The comprehensive information collected formed the basis for the methodology to baseline key indicators of the selected vulnerabilities. The methodology proposed comprised a Bayesian Network (BN) that links data sources for indicators and drivers of change to the prioritised vulnerabilities and was demonstrated for salmon health. A Decision Support System (DSS) with such BN integrated was designed. The current BN and DSS could flag an increased exposure of known hazards that may occur due to changes in indicators but new hazards are not identified. The proposed methodology could be used instead to show trends in vulnerabilities, indicators, increased exposure of known hazards and for scenario studies.

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Detection of clinically important colorectal surgical site infection using Bayesian network

Cited by 51 publications

References 20 publications

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Applicability of a food chain analysis on aquaculture of Atlantic salmon to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks

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