scholarly journals Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 289
Author(s):  
Maria Krechowicz ◽  
Adam Krechowicz
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Assessment Process ◽  
Future Research ◽  
Directional Drilling ◽  
Ann Model ◽  
Learning Models ◽  
Horizontal Directional Drilling ◽  
Risk Assessment Process ◽  
Machine Learning Models

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions.

scholarly journals AB0652 MACHINE LEARNING TO PREDICT EARLY TNF INHIBITOR USERS IN PATIENTS WITH ANKYLOSING SPONDYLITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1620.1-1621
Author(s):  
J. Lee ◽  
H. Kim ◽  
S. Y. Kang ◽  
S. Lee ◽  
Y. H. Eun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ankylosing Spondylitis ◽  
Tnf Inhibitors ◽  
Tnf Inhibitor ◽  
Ann Model ◽  
Learning Models ◽  
Feature Importance ◽  
Importance Analysis ◽  
Baseline Characteristics ◽  
Machine Learning Models

Background:Tumor necrosis factor (TNF) inhibitors are important drugs in treating patients with ankylosing spondylitis (AS). However, they are not used as a first-line treatment for AS. There is an insufficient treatment response to the first-line treatment, non-steroidal anti-inflammatory drugs (NSAIDs), in over 40% of patients. If we can predict who will need TNF inhibitors at an earlier phase, adequate treatment can be provided at an appropriate time and potential damages can be avoided. There is no precise predictive model at present. Recently, various machine learning methods show great performances in predictions using clinical data.Objectives:We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis.Methods:The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early TNF inhibitor users treated by TNF inhibitors within six months of their follow-up (early-TNF users), and the others (non-early-TNF users). Machine learning models were formulated to predict the early-TNF users using the baseline data. Additionally, feature importance analysis was performed to delineate significant baseline characteristics.Results:The numbers of early-TNF and non-early-TNF users were 90 and 509, respectively. The best performing ANN model utilized 3 hidden layers with 50 hidden nodes each; its performance (area under curve (AUC) = 0.75) was superior to logistic regression model, support vector machine, and random forest model (AUC = 0.72, 0.65, and 0.71, respectively) in predicting early-TNF users. Feature importance analysis revealed erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and height as the top significant baseline characteristics for predicting early-TNF users. Among these characteristics, height was revealed by machine learning models but not by conventional statistical techniques.Conclusion:Our model displayed superior performance in predicting early TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.Disclosure of Interests:None declared

scholarly journals Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

2021 ◽  
Author(s):  
Navid Korhani ◽  
Babak Taati ◽  
Andrea Iaboni ◽  
Andrea Sabo ◽  
Sina Mehdizadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Fall Risk ◽  
Learning Models ◽  
Short Term ◽  
Ambient Monitoring ◽  
Clinical Assessments ◽  
People With Dementia ◽  
Time Of Admission ◽  
Machine Learning Models

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.

scholarly journals Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seulkee Lee ◽  
Yeonghee Eun ◽  
Hyungjin Kim ◽  
Hoon-Suk Cha ◽  
Eun-Mi Koh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ankylosing Spondylitis ◽  
Tnf Inhibitor ◽  
Ann Model ◽  
Learning Models ◽  
Feature Importance ◽  
Importance Analysis ◽  
Baseline Characteristics ◽  
Machine Learning Models

AbstractWe aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early-TNF and non-early-TNF users. Machine learning models were formulated to predict the early-TNF users using the baseline data. Feature importance analysis was performed to delineate significant baseline characteristics. The numbers of early-TNF and non-early-TNF users were 90 and 505, respectively. The performance of the ANN model, based on the area under curve (AUC) for a receiver operating characteristic curve (ROC) of 0.783, was superior to logistic regression, support vector machine, random forest, and XGBoost models (for an ROC curve of 0.719, 0.699, 0.761, and 0.713, respectively) in predicting early-TNF users. Feature importance analysis revealed CRP and ESR as the top significant baseline characteristics for predicting early-TNF users. Our model displayed superior performance in predicting early-TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.

scholarly journals A Universal Screening Tool for Dyslexia by a Web-Game and Machine Learning

2022 ◽  
Vol 3 ◽  
Author(s):  
Maria Rauschenberger ◽  
Ricardo Baeza-Yates ◽  
Luz Rello
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Universal Screening ◽  
User Study ◽  
Future Research ◽  
Learning Models ◽  
Early Screening ◽  
Web Based ◽  
Negative Side Effects ◽  
Machine Learning Models

Children with dyslexia have difficulties learning how to read and write. They are often diagnosed after they fail school even if dyslexia is not related to general intelligence. Early screening of dyslexia can prevent the negative side effects of late detection and enables early intervention. In this context, we present an approach for universal screening of dyslexia using machine learning models with data gathered from a web-based language-independent game. We designed the game content taking into consideration the analysis of mistakes of people with dyslexia in different languages and other parameters related to dyslexia like auditory perception as well as visual perception. We did a user study with 313 children (116 with dyslexia) and train predictive machine learning models with the collected data. Our method yields an accuracy of 0.74 for German and 0.69 for Spanish as well as a F1-score of 0.75 for German and 0.75 for Spanish, using Random Forests and Extra Trees, respectively. We also present the game content design, potential new auditory input, and knowledge about the design approach for future research to explore Universal screening of dyslexia. universal screening with language-independent content can be used for the screening of pre-readers who do not have any language skills, facilitating a potential early intervention.

scholarly journals Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft

2018 ◽  
Vol 16 (5) ◽  
pp. 354-364 ◽  
Author(s):  
Shi-Jian Zhang ◽  
Peiqi Meng ◽  
Jieni Zhang ◽  
Peizeng Jia ◽  
Jiuxiang Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Genetic Risk ◽  
Orofacial Cleft ◽  
Learning Models ◽  
Genetic Risk Assessment ◽  
Machine Learning Models

scholarly journals Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3683 ◽  
Author(s):  
Javed Akbar Khan ◽  
Muhammad Irfan ◽  
Sonny Irawan ◽  
Fong Kam Yao ◽  
Md Shokor Abdul Rahaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Sensitivity And Specificity ◽  
Activation Function ◽  
Ann Model ◽  
Learning Models ◽  
Drilling Operation ◽  
Svm Model ◽  
Drilling Operations ◽  
Machine Learning Models

Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients

2019 ◽  
Vol 26 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Xin Wang ◽  
Yu‐Qing Yang ◽  
Si‐Hua Liu ◽  
Xin‐Yu Hong ◽  
Xue‐Feng Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Chinese Patients ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models

2018 ◽  
Vol 1 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Juan M. Banda ◽  
Martin Seneviratne ◽  
Tina Hernandez-Boussard ◽  
Nigam H. Shah
Keyword(s):  
Machine Learning ◽  
Clinical Decision ◽  
Future Research ◽  
Learning Models ◽  
Rule Based ◽  
Research Problems ◽  
Fundamental Research ◽  
Future Research Directions ◽  
Effectiveness Studies ◽  
Machine Learning Models

With the widespread adoption of electronic health records (EHRs), large repositories of structured and unstructured patient data are becoming available to conduct observational studies. Finding patients with specific conditions or outcomes, known as phenotyping, is one of the most fundamental research problems encountered when using these new EHR data. Phenotyping forms the basis of translational research, comparative effectiveness studies, clinical decision support, and population health analyses using routinely collected EHR data. We review the evolution of electronic phenotyping, from the early rule-based methods to the cutting edge of supervised and unsupervised machine learning models. We aim to cover the most influential papers in commensurate detail, with a focus on both methodology and implementation. Finally, future research directions are explored.

Auditor Risk Assessment: Insights from the Academic Literature

2006 ◽  
Vol 20 (2) ◽  
pp. 157-177 ◽  
Author(s):  
Robert D. Allen ◽  
Dana R. Hermanson ◽  
Thomas M. Kozloski ◽  
Robert J. Ramsay
Keyword(s):  
Risk Assessment ◽  
Risk Model ◽  
Academic Research ◽  
Assessment Process ◽  
Future Research ◽  
Audit Risk ◽  
Risk Assessment Process ◽  
Academic Literature ◽  
Fraud Risk ◽  
Briefing Paper

To contribute to the PCAOB project on risk assessment in financial statement audits, we draw on the academic literature to offer insights and conclusions on the risk-assessment process. We use the PCAOB's (2005) recent briefing paper on risk assessment as the organizing framework for our literature review, and we examine academic auditing literature addressing topics including business risk, inherent risk, control risk, fraud risk, linking risk assessments to subsequent testing, and the audit risk model. Overall, we believe that the results of academic research are consistent with the PCAOB staff's apparent reconsideration of the auditor's risk-assessment process. We conclude with identification of future research topics and recognition of barriers to performing research that is relevant to standard setters.

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