scholarly journals Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning

2019 ◽  
Vol 10 ◽  
Author(s):  
Xiaolan Mo ◽  
Xiujuan Chen ◽  
Hongwei Li ◽  
Jiali Li ◽  
Fangling Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Clinical Response ◽  
Accurate Prediction ◽  
Methotrexate Treatment

Deficits of vitamin D are strongly associated with methotrexate treatment in patients with juvenile idiopathic arthritis

2015 ◽  
Author(s):  
Adrian Goralczyk ◽  
Jerzy Konstantynowicz ◽  
Pawel Abramowicz ◽  
Elzbieta Dobrenko ◽  
Edyta Babinska-Malec
Keyword(s):  
Vitamin D ◽  
Juvenile Idiopathic Arthritis ◽  
Methotrexate Treatment

scholarly journals Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

2019 ◽  
Vol 78 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Learning Approaches ◽  
Data Set ◽  
Immune Signature ◽  
Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

2018 ◽  
Vol 45 (5) ◽  
pp. 2243-2251 ◽  
Author(s):  
Baozhou Sun ◽  
Dao Lam ◽  
Deshan Yang ◽  
Kevin Grantham ◽  
Tiezhi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

DFT-Machine Learning Approach for Accurate Prediction of pKa

2021 ◽  
Author(s):  
Robin Lawler ◽  
Yao-Hao Liu ◽  
Nessa Majaya ◽  
Omar Allam ◽  
Hyunchul Ju ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4952
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Machine Learning ◽  
Cast Iron ◽  
Tool Wear ◽  
Flank Wear ◽  
Accurate Prediction ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Drilling Process ◽  
Extreme Gradient Boosting

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm’s hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R2 = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R2 = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

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