Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study

ObjectivesTo develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.DesignRetrospective observational study.SettingED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020.ParticipantsAdult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits.Main outcome measuresThe primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED.ResultsIn 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%.ConclusionsBoth models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

Determining the residuary resistance per unit weight of displacement is one of the key factors in the design of vessels. In this paper, the authors utilize two novel methods – Symbolic Regression (SR) and Gradient Boosted Trees (GBT) to achieve a model which can be used to calculate the value of residuary resistance per unit weight, of displacement from the longitudinal position of the center of buoyancy, prismatic coefficient, length-displacement ratio, beam-draught ratio, length-beam ratio, and Froude number. This data is given as results of 308 experiments provided as a part of a publicly available dataset. The results are evaluated using the coefficient of determination (R2) and Mean Absolute Percentage Error (MAPE). Pre-processing, in the shape of correlation analysis combined with variable elimination and variable scaling, is applied to the dataset. The results show that while both methods achieve regression results, the result of regression of SR is relatively poor in comparison to GBT. Both methods provide slightly poorer, but comparable results to previous research focussing on the use of “black-box” methods, such as neural networks. The elimination of variables does not show a high influence on the modeling performance in the presented case, while variable scaling does achieve better results compared to the models trained with the non-scaled dataset.

Predictive Analysis of the Enrolment of Elementary Schools Using Regression Algorithms

— By fitting a linear equation to observable values, linear regression determines the relationship between two variables. The Department of Education enrollment data in the Philippines, specifically in the School Division of Batangas, is needed to produce modules. The data collected is from the division office, where data cleaning was applied. Deep Learning, Decision Tree, Random Forest, Gradient Boosted Tree, Support Vector Machine, and Linear Regression were used to perform the prediction, and linear regression performed the best with an absolute value of 14.465 and a relative error of 84.81%. Keywords— Prediction, Information Management, Linear Regression, Cloud Computing, LDM

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance. Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique

In geotechnical engineering, there is a need to propose a practical, reliable and accurate way for the estimation of pile bearing capacity. A direct measure of this parameter is difficult and expensive to achieve on-site, and needs a series of machine settings. This study aims to introduce a process for selecting the most important parameters in the area of pile capacity and to propose several tree-based techniques for forecasting the pile bearing capacity, all of which are fully intelligent. In terms of the first objective, pile length, hammer drop height, pile diameter, hammer weight, and N values of the standard penetration test were selected as the most important factors for estimating pile capacity. These were then used as model inputs in different tree-based techniques, i.e., decision tree (DT), random forest (RF), and gradient boosted tree (GBT) in order to predict pile friction bearing capacity. This was implemented with the help of 130 High Strain Dynamic Load tests which were conducted in the Kepong area, Malaysia. The developed tree-based models were assessed using various statistical indices and the best performance with the lowest system error was obtained by the GBT technique. The coefficient of determination (R2) values of 0.901 and 0.816 for the train and test parts of the GBT model, respectively, showed the power and capability of this tree-based model in estimating pile friction bearing capacity. The GBT model and the input selection process proposed in this research can be introduced as a new, powerful, and practical methodology to predict pile capacity in real projects.

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance. Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

Metagenomic evidence for a polymicrobial signature of sepsis

Our understanding of the host component of sepsis has made significant progress. However, detailed study of the microorganisms causing sepsis, either as single pathogens or microbial assemblages, has received far less attention. Metagenomic data offer opportunities to characterize the microbial communities found in septic and healthy individuals. In this study we apply gradient-boosted tree classifiers and a novel computational decontamination technique built upon SHapley Additive exPlanations (SHAP) to identify microbial hallmarks which discriminate blood metagenomic samples of septic patients from that of healthy individuals. Classifiers had high performance when using the read assignments to microbial genera [area under the receiver operating characteristic (AUROC=0.995)], including after removal of species ‘culture-confirmed’ as the cause of sepsis through clinical testing (AUROC=0.915). Models trained on single genera were inferior to those employing a polymicrobial model and we identified multiple co-occurring bacterial genera absent from healthy controls. While prevailing diagnostic paradigms seek to identify single pathogens, our results point to the involvement of a polymicrobial community in sepsis. We demonstrate the importance of the microbial component in characterising sepsis, which may offer new biological insights into the aetiology of sepsis, and ultimately support the development of clinical diagnostic or even prognostic tools.

Differential Evolution Based Machine Learning Scheme for Secure Cooperative Spectrum Sensing System

The secondary users (SUs) in cognitive radio networks (CRNs) can obtain reliable spectrum sensing information of the primary user (PU) channel using cooperative spectrum sensing (CSS). Multiple SUs share their sensing observations in the CSS system to tackle fading and shadowing conditions. The presence of malicious users (MUs) may pose threats to the performance of CSS due to the reporting of falsified sensing data to the fusion center (FC). Different categories of MUs, such as always yes, always no, always opposite, and random opposite, are widely investigated by researchers. To this end, this paper proposes a hybrid boosted tree algorithm (HBTA)-based solution that combines the differential evolution (DE) and boosted tree algorithm (BTA) to mitigate the effects of MUs in the CSS systems, leading to reliable sensing results. An optimized threshold and coefficient vector, determined against the SUs employing DE, is utilized to train the BTA. The BTA is a robust ensembling machine learning (ML) technique gaining attention in spectrum sensing operations. To show the effectiveness of the proposed scheme, extensive simulations are performed at different levels of signal-to-noise-ratios (SNRs) and with different sensing samples, iteration levels, and population sizes. The simulation results show that more reliable spectrum decisions can be achieved compared to the individual utilization of DE and BTA schemes. Furthermore, the obtained results show the minimum sensing error to be exhibited by the proposed HBTA employing a DE-based solution to train the BTA. Additionally, the proposed scheme is compared with several other CSS schemes such as simple DE, simple BTA, maximum gain combination (MGC), particle swarm optimization (PSO), genetic algorithm (GA), and K-nearest neighbor (KNN) algorithm-based soft decision fusion (SDF) schemes to validate its effectiveness.