scholarly journals Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

2021 ◽  
Author(s):  
Pedro Baqui ◽  
Valerio Marra ◽  
Ahmed M. Alaa ◽  
Ioana Bica ◽  
Ari Ercole ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Careful Study ◽  
Structural Factors ◽  
Hospital Funding ◽  
Complex Interdependence ◽  
Funding Model ◽  
Preventative Measures ◽  
Increased Risk ◽  
Complex Scenario

BackgroundThe COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil’s social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically.MethodsWe consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics.FindingsThe XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95%CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors.InterpretationSocioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.FundingNone.

scholarly journals Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pedro Baqui ◽  
Valerio Marra ◽  
Ahmed M. Alaa ◽  
Ioana Bica ◽  
Ari Ercole ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Careful Study ◽  
Structural Factors ◽  
Hospital Funding ◽  
Complex Interdependence ◽  
Funding Model ◽  
Preventative Measures ◽  
Increased Risk ◽  
Complex Scenario

AbstractThe COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil’s social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.

scholarly journals Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning

2020 ◽  
Author(s):  
F. P. Chmiel ◽  
M. Azor ◽  
F. Borca ◽  
M. J. Boniface ◽  
D. K. Burns ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Departments ◽  
Risk Groups ◽  
Machine Learning Algorithms ◽  
Care Indicator ◽  
Number Of Patients ◽  
Increased Risk ◽  
Single Centre Study ◽  
Patients At Risk

ABSTRACTShort-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance can help reduce the number of patients with missed or undertreated illness or injury, and could support appropriate discharges with focused interventions. In this manuscript we present a retrospective, single-centre study where we create and evaluate a machine-learnt classifier trained to identify patients at risk of reattendance within 72 hours of discharge from an emergency department. On a patient hold-out test set, our highest performing classifier obtained an AUROC of 0.748 and an average precision of 0.250; demonstrating that machine-learning algorithms can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. In parallel to our predictive model we train an explanation model, capable of explaining predictions at an attendance level, which can be used to help inform the design of interventional strategies.

scholarly journals Cardiovascular Autonomic Function Changes and Predictors During a 2-Year Physical Activity Program in Rheumatoid Arthritis: A PARA 2010 Substudy

2021 ◽  
Vol 8 ◽  
Author(s):  
David Hupin ◽  
Philip Sarajlic ◽  
Ashwin Venkateshvaran ◽  
Cecilia Fridén ◽  
Birgitta Nordgren ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Physical Activity ◽  
Machine Learning ◽  
Heart Rate ◽  
Autonomic Function ◽  
Maximal Exercise ◽  
Machine Learning Algorithms ◽  
Physical Activity Program ◽  
Activity Program ◽  
Increased Risk

Background: Chronic inflammation leads to autonomic dysfunction, which may contribute to the increased risk of cardiovascular diseases (CVD) in patients with rheumatoid arthritis (RA). Exercise is known to restore autonomic nervous system (ANS) activity and particularly its parasympathetic component. A practical clinical tool to assess autonomic function, and in particular parasympathetic tone, is heart rate recovery (HRR). The aim of this substudy from the prospective PARA 2010 study was to determine changes in HRR post-maximal exercise electrocardiogram (ECG) after a 2-year physical activity program and to determine the main predictive factors associated with effects on HRR in RA.Methods: Twenty-five participants performed physiotherapist-guided aerobic and muscle-strengthening exercises for 1 year and were instructed to continue the unsupervised physical activity program autonomously in the next year. All participants were examined at baseline and at years 1 and 2 with a maximal exercise ECG on a cycle ergometer. HRR was measured at 1, 2, 3, 4, and 5 min following peak heart rate during exercise. Machine-learning algorithms with the elastic net linear regression models were performed to predict changes in HRR1 and HRR2 at 1 year and 2 years of the PARA program.Results: Mean age was 60 years, range of 41–73 years (88% women). Both HRR1 and HRR2 increased significantly from baseline to year 1 with guided physical activity and decreased significantly from year 1 to year 2 with unsupervised physical activity. Blood pressure response to exercise, low BMI, and muscular strength were the best predictors of HRR1/HRR2 increase during the first year and HRR1/HRR2 decrease during the second year of the PARA program.Conclusion: ANS activity in RA assessed by HRR was improved by guided physical activity, and machine learning allowed to identify predictors of the HRR response at the different time points. HRR could be a relevant marker of the effectiveness of physical activity recommended in patients with RA at high risk of CVD. Very inactive and/or high CVD risk RA patients may get substantial benefits from a physical activity program.

scholarly journals Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

2020 ◽  
Vol 17 (24) ◽  
pp. 9347 ◽  
Author(s):  
Md Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Tasadduq Imam ◽  
Steven Gordon
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Service Providers ◽  
Detection System ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Increased Risk ◽  
Iot Devices

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

scholarly journals Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3198 ◽  
Author(s):  
Victor Garcia-Font ◽  
Carles Garrigues ◽  
Helena Rifà-Pous
Keyword(s):  
Machine Learning ◽  
Sensor Networks ◽  
Smart City ◽  
Smart Cities ◽  
Technical Difficulty ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques ◽  
Complex Scenario

Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments.

scholarly journals The Effect of Individual Musculoskeletal Conditions on Depression: Updated Insights From an Irish Longitudinal Study on Aging

2021 ◽  
Vol 8 ◽  
Author(s):  
Wenyi Jin ◽  
Zilin Liu ◽  
Yubiao Zhang ◽  
Zhifei Che ◽  
Mingyong Gao
Keyword(s):  
Machine Learning ◽  
Depressive Symptoms ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Machine Learning Algorithms ◽  
Mineral Density ◽  
Classification Approach ◽  
Musculoskeletal Conditions ◽  
Increased Risk ◽  
Mediation Models

Few longitudinal studies have systematically investigated whether or how individual musculoskeletal conditions (IMCs) convey risks for negative psychological health outcomes, and approaches to assess such risk in the older population are lacking. In this Irish nationally representative longitudinal prospective study of 6,715 individuals aged 50 and above, machine learning algorithms and various models, including mediation models, were employed to elaborate the underlying mechanisms of IMCs leading to depression and to develop an IMC-induced negative psychological risk (IMCPR) classification approach. Resultantly, arthritis [odds ratio (95% confidence interval): 2.233 (1.700–2.927)], osteoporosis [1.681 (1.133–2.421)], and musculoskeletal chronic pain [MCP, 2.404 (1.838–3.151)] were found to increase the risk of depression after 2 years, while fracture and joint replacement did not. Interestingly, mediation models further demonstrated that arthritis per se did not increase the risk of depression; such risk was augmented only when arthritis-induced restrictions of activities (ARA) existed [proportion of mediation: 316.3% (ARA of usual), 213.3% (ARA of social and leisure), and 251.3% (ARA of sleep)]. The random forest algorithm attested that osteoarthritis, not rheumatoid arthritis, contributed the most to depressive symptoms. Moreover, bone mineral density was negatively associated with depressive symptoms. Systemic pain contributed the most to the increased risk of depression, followed by back, knee, hip, and foot pain (mean Gini-Index: 3.778, 2.442, 1.980, 1.438, and 0.879, respectively). Based on the aforementioned findings, the IMCPR classification approach was developed using an interpretable machine learning model, which stratifies participants into three grades. Among the IMCPR grades, patients with a grade of “severe” had higher odds of depression than those with a “mild” [odds ratio (95% confidence interval): 4.055 (2.907–5.498)] or “moderate” [3.584 (2.101–5.883)] grade. Females with a “severe” grade had higher odds of depression by 334.0% relative to those with a “mild” grade, while males had a relative risk of 258.4%. In conclusion, the present data provide systematic insights into the IMC-induced depression risk and updated the related clinical knowledge. Furthermore, the IMCPR classification approach could be used as an effective tool to evaluate this risk.

scholarly journals Using real-world transaction data to identify money laundering: Leveraging traditional regression and machine learning techniques

2021 ◽  
pp. 1-11
Author(s):  
Daniel A. Harris ◽  
Kyla L. Pyndiura ◽  
Shelby L. Sturrock ◽  
Rebecca A.G. Christensen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Money Laundering ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Binary Outcome ◽  
Learning Techniques ◽  
Increased Risk

Money laundering is a pervasive legal and economic problem that hides criminal activity. Identifying money laundering is a priority for both banks and governments, thus, machine learning algorithms have emerged as a possible strategy to detect suspicious financial activity within financial institutions. We used traditional regression and supervised machine learning techniques to identify bank customers at an increased risk of committing money laundering. Specifically, we assessed whether model performance differed across varying operationalizations of the outcome (e.g., multinomial vs. binary classification) and determined whether the inclusion of investigator-derived novel features (e.g., averages across existing features) could improve model performance. We received two proprietary datasets from Scotiabank, a large bank headquartered in Canada. The datasets included customer account information (N = 4,469) and customers’ monthly transaction histories (N = 2,827) from April 15, 2019 to April 15, 2020. We implemented traditional logistic regression, logistic regression with LASSO regularization (LASSO), K-nearest neighbours (KNN), and extreme gradient boosted models (XGBoost). Results indicated that traditional logistic regression with a binary outcome, conducted with investigator-derived novel features, performed the best with an F1 score of 0.79 and accuracy of 0.72. Models with a binary outcome had higher accuracy than the multinomial models, but the F1 scores yielded mixed results. For KNN and XGBoost, we observed little change or worsening performance after the introduction of the investigator-derived novel features. However, the investigator-derived novel features improved model performance for LASSO and traditional logistic regression. Our findings demonstrate that investigators should consider different operationalizations of the outcome, where possible, and include novel features derived from existing features to potentially improve the detection of customer at risk of committing money laundering.

scholarly journals Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation

2020 ◽  
Vol 16 (1) ◽  
pp. 43-52
Author(s):  
Ryoko Suzuki ◽  
Jun Katada ◽  
Sreeram Ramagopalan ◽  
Laura McDonald
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Nonvalvular Atrial Fibrillation ◽  
New Approach ◽  
Increased Risk ◽  
Sensitivity Specificity ◽  
Retrospective Database

Aim: Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of stroke however many patients are diagnosed after onset. This study assessed the potential of machine-learning algorithms to detect NVAF. Materials & methods: A retrospective database study using a Japanese claims database. Patients with and without NVAF were selected. 41 variables were included in different classification algorithms. Results: Machine learning algorithms identified NVAF with an area under the curve of >0.86; corresponding sensitivity/specificity was also high. The stacking model which combined multiple algorithms outperformed single-model approaches (area under the curve ≥0.90, sensitivity/specificity ≥0.80/0.82), although differences were small. Conclusion: Machine-learning based algorithms can detect atrial fibrillation with accuracy. Although additional validation is needed, this methodology could encourage a new approach to detect NVAF.

Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis

Heart ◽  
2020 ◽  
Vol 106 (13) ◽  
pp. 1007-1014 ◽  
Author(s):  
Gerhard Paul Diller ◽  
Stefan Orwat ◽  
Julius Vahle ◽  
Ulrike M M Bauer ◽  
Aleksandra Urban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Right Ventricular ◽  
Tetralogy Of Fallot ◽  
Adverse Outcome ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Right Atrial ◽  
Ventricular Ejection Fraction ◽  
Increased Risk

ObjectiveTo assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR).MethodsWe included 372 patients with ToF who had undergone CMR imaging as part of a nationwide prospective study. Cine loops were retrieved and subjected to automatic deep learning (DL)-based image analysis, trained on independent, local CMR data, to derive measures of cardiac dimensions and function. This information was combined with established clinical parameters and ECG markers of prognosis.ResultsOver a median follow-up period of 10 years, 23 patients experienced an endpoint of death/aborted cardiac arrest or documented ventricular tachycardia (defined as >3 documented consecutive ventricular beats). On univariate Cox analysis, various DL parameters, including right atrial median area (HR 1.11/cm², p=0.003) and right ventricular long-axis strain (HR 0.80/%, p=0.009) emerged as significant predictors of outcome. DL parameters were related to adverse outcome independently of left and right ventricular ejection fraction and peak oxygen uptake (p<0.05 for all). A composite score of enlarged right atrial area and depressed right ventricular longitudinal function identified a ToF subgroup at significantly increased risk of adverse outcome (HR 2.1/unit, p=0.007).ConclusionsWe present data on the utility of machine learning algorithms trained on external imaging datasets to automatically estimate prognosis in patients with ToF. Due to the automated analysis process these two-dimensional-based algorithms may serve as surrogates for labour-intensive manually attained imaging parameters in patients with ToF.

Predictors of suicide attempt in patients with obsessive-compulsive disorder: an exploratory study with machine learning analysis

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Neusa Aita Agne ◽  
Caroline Gewehr Tisott ◽  
Pedro Ballester ◽  
Ives Cavalcante Passos ◽  
Ygor Arzeno Ferrão
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Suicide Attempt ◽  
Obsessive Compulsive Disorder ◽  
Elastic Net ◽  
Machine Learning Algorithms ◽  
Obsessive Compulsive ◽  
Sociodemographic Variables ◽  
Compulsive Disorder ◽  
Increased Risk

Abstract Background Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear – whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm. Methods A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables. Results The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95. Conclusions This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.

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