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 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 Personalized prediction of early childhood asthma persistence: A machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247784
Author(s):  
Saurav Bose ◽  
Chén C. Kenyon ◽  
Aaron J. Masino
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Childhood Asthma ◽  
Persistent Asthma ◽  
Clinical Information ◽  
Asthma Diagnosis ◽  
Learning Models ◽  
Feature Importance ◽  
Importance Analysis ◽  
Machine Learning Models

Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience symptom resolution and it remains difficult to identify individuals whose symptoms will persist. Our objective was to develop machine learning models to identify which individuals diagnosed with asthma before age 5 continue to experience asthma-related visits. We curated a retrospective dataset for 9,934 children derived from electronic health record (EHR) data. We trained five machine learning models to differentiate individuals without subsequent asthma-related visits (transient diagnosis) from those with asthma-related visits between ages 5 and 10 (persistent diagnosis) given clinical information up to age 5 years. Based on average NPV-Specificity area (ANSA), all models performed significantly better than random chance, with XGBoost obtaining the best performance (0.43 mean ANSA). Feature importance analysis indicated age of last asthma diagnosis under 5 years, total number of asthma related visits, self-identified black race, allergic rhinitis, and eczema as important features. Although our models appear to perform well, a lack of prior models utilizing a large number of features to predict individual persistence makes direct comparison infeasible. However, feature importance analysis indicates our models are consistent with prior research indicating diagnosis age and prior health service utilization as important predictors of persistent asthma. We therefore find that machine learning models can predict which individuals will experience persistent asthma with good performance and may be useful to guide clinician and parental decisions regarding asthma counselling in early childhood.

scholarly journals Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Seulkee Lee ◽  
Seonyoung Kang ◽  
Yeonghee Eun ◽  
Hong-Hee Won ◽  
Hyungjin Kim ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Logistic Regression ◽  
Ankylosing Spondylitis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Performance ◽  
Learning Models ◽  
Independent Test ◽  
Machine Learning Models

Abstract Background Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. Methods Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model. Results The RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725–0.730] vs. 0.689 [0.606–0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576–0.658] vs. 0.565 [0.493–0.605], F1 score 0.841 [0.837–0.843] vs. 0.803 [0.732–0.828], AUC of the precision-recall curve 0.808 [0.763–0.829] vs. 0.754 [0.714–0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases. Conclusions RF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.

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 Permutation-based identification of important biomarkers for complex diseases via machine learning models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xinlei Mi ◽  
Baiming Zou ◽  
Fei Zou ◽  
Jianhua Hu
Keyword(s):  
Machine Learning ◽  
Human Disease ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Individual Feature ◽  
Learning Models ◽  
Efficient Manner ◽  
Feature Importance ◽  
Machine Learning Models

AbstractStudy of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting each individual feature due to their sophisticated algorithms. However, identifying important biomarkers is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in complex frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available at https://github.com/SkadiEye/deepTL) is implemented in a computationally efficient manner, without model refitting. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also improves the prediction accuracy of machine learning models. With the application to the Cancer Genome Atlas kidney tumor data and the HITChip atlas data, PermFIT demonstrates its practical usage in identifying important biomarkers and boosting model prediction performance.

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.

scholarly journals Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 4846-4853 ◽  
Author(s):  
Patrick Schwab ◽  
Djordje Miladinovic ◽  
Walter Karlen
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Learning Models ◽  
Mixture Of Experts ◽  
Single Model ◽  
New Approach ◽  
Domain Experts ◽  
Mixtures Of Experts ◽  
Feature Importance ◽  
Machine Learning Models

Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-theart methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.

scholarly journals Unraveling the dynamic importance of county-level features in trajectory of COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qingchun Li ◽  
Yang Yang ◽  
Wanqiu Wang ◽  
Sanghyeon Lee ◽  
Xin Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Driven ◽  
County Level ◽  
Learning Models ◽  
Population Densities ◽  
Social Network Structure ◽  
The Social ◽  
Heterogeneous Features ◽  
Feature Importance ◽  
Machine Learning Models

AbstractThe objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle.

scholarly journals Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021 ◽  
Vol 11 (2) ◽  
pp. 210
Author(s):  
Monika Kaczorowska ◽  
Małgorzata Plechawska-Wójcik ◽  
Mikhail Tokovarov
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Cognitive Workload ◽  
Digit Symbol Substitution Test ◽  
Learning Models ◽  
Interpretable Machine Learning ◽  
Feature Importance ◽  
The Brain ◽  
Machine Learning Models

The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied.

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