Machine learning, infection, microbial toxins profile and health monitoring pre/post general surgeries during COVID-19 pandemic

Although almost 2 years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various research in order to eliminate or reduce the problems caused by this disease. Surgery is one of the sciences that plays a critical role in this regard. Both physicians and patients should pay attention to the potent steps of different infections’ key-points during pre/post-general surgeries in the case of preventing or accelerating the healing process of nosocomial acquired COVID-19. The relationship between COVID-19 and general surgical events is one of the factors that could directly or indirectly play a key role in the body's resilience to COVID-19. In this article, we introduce a link between pre/post-general surgery steps, human microbial toxin profiles, and the incidence of acquired COVID-19 in patients. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies to assist health providers in choosing the best decision based on a patient’s history. 

Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study

Funding: This research was funded by the Joint Swiss–Portuguese Academic Program from the University of Applied Sciences and Arts Western Switzerland (HES-SO) and the Fundação para a Ciência e Tecnologia (FCT). S.G.P. also acknowledges FCT for her direct funding (CEECINST/00051/2018) and her research unit (UIDB/05704/2020). Funders were not involved in the study design, data pre-processing, data analysis, interpretation, or report writing. Author contributions: R.G. and A.B. designed and implemented the models, and ran the experiments and analyses. R.G. and D.T. wrote the manuscript draft. D.T. and S.G.P. conceptualized the experiments and acquired funding. R.G., D.P., and S.G.P. curated the data. R.G., A.B., D.P., and D.T. analyzed the data. All authors reviewed and approved the manuscript. Competing interests: The authors declare that they have no competing interests.Background: While Enterobacteriaceae bacteria are commonly found in the healthy human gut, their colonization of other body parts can potentially evolve into serious infections and health threats. We investigate a graph-based machine learning model to predict risks of inpatient colonization by multidrug-resistant (MDR) Enterobacteriaceae. Methods: Colonization prediction was defined as a binary task, where the goal is to predict whether a patient is colonized by MDR Enterobacteriaceae in an undesirable body part during their hospital stay. To capture topological features, interactions among patients and healthcare workers were modeled using a graph structure, where patients are described by nodes and their interactions are described by edges. Then, a graph neural network (GNN) model was trained to learn colonization patterns from the patient network enriched with clinical and spatiotemporal features. Results: The GNN model achieves performance between 0.91 and 0.96 area under the receiver operating characteristic curve (AUROC) when trained in inductive and transductive settings, respectively, up to 8% above a logistic regression baseline (0.88). Comparing network topologies, the configuration considering ward-related edges (0.91 inductive, 0.96 transductive) outperforms the configurations considering caregiver-related edges (0.88, 0.89) and both types of edges (0.90, 0.94). For the top 3 most prevalent MDR Enterobacteriaceae, the AUROC varies from 0.94 for Citrobacter freundii up to 0.98 for Enterobacter cloacae using the best-performing GNN model. Conclusion: Topological features via graph modeling improve the performance of machine learning models for Enterobacteriaceae colonization prediction. GNNs could be used to support infection prevention and control programs to detect patients at risk of colonization by MDR Enterobacteriaceae and other bacteria families.info:eu-repo/semantics/publishedVersio

Electronically assisted surveillance systems of healthcare-associated infections:a systematic review

Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital- wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

Electronically assisted surveillance systems of healthcare-associated infections: A systematic review

Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37 1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

BackgroundSurveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.ObjectivesTo give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.MethodsIn this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.ResultsA total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.ConclusionsElectronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

Predictive analytics na infeção hospitalar

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de InformaçãoAs infeções nosocomiais e a resistência antimicrobiana provocam um elevado número de morbidade e mortalidade nos pacientes hospitalizados. A Comissão de Controlo de Infeção (CCI) define medidas para combater a propagação de infeção nosocomial para outros doentes. No entanto, o controlo de infeção é ineficaz, uma vez que a sua deteção é feita de forma manual e por vezes tardia. A utilização de Predictive Analytics surge como uma possível solução para este problema, dado que permite a previsão automática e atempada de infeção, melhorando o tempo de resposta, e consequentemente, o controlo de infeção hospitalar. Nesta dissertação o principal objetivo passou por desenvolver modelos preditivos com boa capacidade de previsão de infeção nosocomial, a partir de técnicas de Data Mining (DM) e Machine Learning (ML). O desenvolvimento dos modelos de previsão foi realizado em contexto local e offline, e com dados reais provenientes do Hospital da Senhora da Oliveira de Guimarães. Deste modo, foram adotadas as metodologias Design Science Research Methodology (DSRM) e Cross-Industry Standard Process for Data Mining (CRISP-DM). O DSRM foi aplicado na investigação deste projeto de dissertação e o CRISP-DM foi usado para a aplicação de técnicas de DM. A abordagem de DM aplicada foi a Classificação e para que os modelos de DM pudessem ser criados, foram selecionadas seis técnicas baseadas em Árvores de Decisão (AD), Random Forest (RF), Redes Neuronais (RN), Naive Bayes (NB), Support Vector Machine (SVM) e Regressão Logística (RL). A avaliação dos modelos foi efetuada a partir da Matriz de Confusão, que permitiu a definição de sete métricas, Acuidade, Sensibilidade, Especificidade, Precisão, F1-Score, Índice Kappa e Curva AUC. Destas sete, a Acuidade e Sensibilidade, foram selecionadas como as mais importantes na decisão do melhor modelo. Os modelos de previsão concebidos apresentam uma grande capacidade de previsão de infeção nosocomial, com valores de Acuidade entre 71.56% a 99.37% e valores de Sensibilidade superiores a 90%. Os resultados obtidos são positivos e podem ajudar os profissionais de saúde na tomada de decisão ao nível da gestão e controlo de infeção nosocomial.Nosocomial infections and antimicrobial resistance cause a high number of morbidity and mortality in hospitalized patients. The Infection Control Commission (ICC) defines measures to combat the spread of nosocomial infection to other patients. However, the infection control is ineffective, since its detection is done manually and sometimes late. The use of Predictive Analytics is a possible solution to this problem, since it allows the automatic and timely prediction of infection, improving the response time, and consequently, the infection control of the hospital. The main objective of this dissertation was to develop predictive models with good predicitve ability for nosocomial infection, based on Data Mining (DM) and Machine Learning techniques. The development of the predictive models was performed in a local and offline context, and with real data from Hospital da Senhora da Oliveira in Guimarães. Thus, the Design Science Research Methodology (DSRM) and Cross Industry Standard Process for Data Mining (CRISP-DM) methodologies were adopted. The DSRM was applied in the research of this dissertation project and the CRISP-DM was used for the application of DM techniques. The DM approach applied was Classification and so that the DM models could be created, six techniques were selected based on Decision Trees (DT), Random Forest (RF), Neural Networks (NN), Naive Bayes (NB), Support Vector Machine (SVM) and Logistic Regression (LR). The evaluation of the modes was performed from the Confusion Matrix, which allowed the definition of seven metrics, Accuracy, Recall, Specificity, Precision, F1-Score, Kappa Statistic and AUC Curve. Of these seven, Accuracy and Recall were selected as the most importante in deciding the best model. The designed prediction models show a high predictive capacity for nocomial infection, with Accuracy values between 71.56% and 99.37%, and Recall values above 90%. The results obtained are positive and can help heath professionals in decision-making in nosocomial infection control and management

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Risk Factors Affecting Death from Hospital-Acquired Infections in Trauma Patients: Association Rule Mining

Introduction: Trauma patients are potentially at high risk of acquiring infections in hospitals,which is the main cause of in-hospital mortality. The aim of this study was to identify the riskfactors contributing to death from hospital-acquired infections in trauma patients by datamining techniques.Methods: This is a cohort study. A total of 549 trauma patients with nosocomial infectionwho were admitted to Shiraz trauma hospital between 2017 and 2018 were studied. Sex,age, mechanism of injury, body region injured, injury severity score, length of stay, typeof intervention, infection day after admission, microorganism cause of infections, andthe outcomes were collected. Association rule mining techniques were applied to extractknowledge from the data set. The IBM SPSS Modeler data mining software version 18.0 wasused as a tool for data mining of the trauma patients with hospital queried infections database.Results: The age older than 65, surgical site infection skin, bloodstream infection, mechanisminjury of car accident, invasive intervention of tracheal intubation, injury severity score higherthan 16, and multiple injuries with higher than 71 percent confidence level were associatedwith in-hospital mortality. The relationship between those predicators and death amonghospital-acquired infection was strong (Lift value >1).Conclusion: Factors such as increasing age, tracheal intubation, mechanical ventilator,surgical site infection skin, upper respiratory infection are associated with death fromhospital-acquired infections in trauma patients by data mining

Live Genomics for Pathogen Monitoring in Public Health

Whole genome analysis based on next generation sequencing (NGS) now represents an affordable framework in public health systems. Robust analytical pipelines of genomic data provides in a short lapse of time (hours) information about taxonomy, comparative genomics (pan-genome) and single polymorphisms profiles. Pathogenic organisms of interest can be tracked at the genomic level, allowing monitoring at one-time several variables including: epidemiology, pathogenicity, resistance to antibiotics, virulence, persistence factors, mobile elements and adaptation features. Such information can be obtained not only at large spectra, but also at the “local” level, such as in the event of a recurrent or emergency outbreak. This paper reviews the state of the art in infection diagnostics in the context of modern NGS methodologies. We describe how actuation protocols in a public health environment will benefit from a “streaming approach” (pipeline). Such pipeline would include NGS data quality assessment, data mining for comparative analysis, searching differential genetic features, such as virulence, resistance persistence factors and mutation profiles (SNPs and InDels) and formatted “comprehensible” results. Such analytical protocols will enable a quick response to the needs of locally circumscribed outbreaks, providing information on the causes of resistance and genetic tracking elements for rapid detection, and monitoring actuations for present and future occurrences