scholarly journals Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251365
Author(s):  
Amita Sharma ◽  
Willem J. M. I. Verbeke
Keyword(s):  
Machine Learning ◽  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
Variable Importance ◽  
Mental Illnesses ◽  
World Health ◽  
Learning Models ◽  
Clinical Biomarkers ◽  
Health Organization ◽  
Machine Learning Models

Anxiety disorders are a group of mental illnesses that cause constant and overwhelming feelings of anxiety and fear. Excessive anxiety can make an individual avoid work, school, family get-togethers, and other social situations that in turn might amplify these symptoms. According to the World Health Organization (WHO), one in thirteen persons globally suffers from anxiety. It is high time to understand the roles of various clinical biomarker measures that can diagnose the types of anxiety disorders. In this study, we apply machine learning (ML) techniques to understand the importance of a set of biomarkers with four types of anxiety disorders—Generalized Anxiety Disorder (GAD), Agoraphobia (AP), Social Anxiety Disorder (SAD) and Panic Disorder (PD). We used several machine learning models and extracted the variable importance contributing to a type of anxiety disorder. The study uses a sample of 11,081 Dutch citizens’ data collected by the Lifelines, Netherlands. The results show that there are significant and low correlations among GAD, AP, PD and SAD and we extracted the variable importance hierarchy of biomarkers with respect to each type of anxiety disorder which will be helpful in designing the experimental setup for clinical trials related to influence of biomarkers on type of anxiety disorder.

Predicting the Linguistic Accessibility of Chinese Health Translations: Using Machine Learning Algorithms (Preprint)

2021 ◽  
Author(s):  
Meng Ji ◽  
Pierrette Bouillon
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Health Resources ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Learning Models ◽  
The World ◽  
Health Organization ◽  
C5.0 Decision Tree ◽  
Machine Learning Models

BACKGROUND Linguistic accessibility has important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been under-studied. OBJECTIVE Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. METHODS Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from China Centre for Disease Control and Prevention. RESULTS Pairwise adjusted t test showed that three new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of AUC: C5.0 decision tree (p=0.000, 95% CI: -0.249, -0.152), random forest (p=0.000, 95% CI: 0.139, 0.239) and XGBoost Tree (p=0.000, 95% CI: 0.099, 0.193). There was however no significant difference between C5.0 decision tree and random forest (p=0.513). Extreme gradient boost tree was the best model having achieved statistically significant improvement over the C5.0 model (p=0.003) and the Random Forest model (p=0.006) at the adjusted Bonferroni p value at 0.008. CONCLUSIONS The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.

scholarly journals Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies

2021 ◽  
Vol 6 ◽  
pp. 309
Author(s):  
Paul Mwaniki ◽  
Timothy Kamanu ◽  
Samuel Akech ◽  
M. J. C Eijkemans
Keyword(s):  
Machine Learning ◽  
Epidemiological Studies ◽  
Chest Radiographs ◽  
World Health ◽  
Data Sets ◽  
Learning Models ◽  
Middle Income ◽  
Training Models ◽  
Model Training ◽  
Machine Learning Models

Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.

scholarly journals Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

2021 ◽  
Vol 13 (18) ◽  
pp. 3790
Author(s):  
Khang Chau ◽  
Meredith Franklin ◽  
Huikyo Lee ◽  
Michael Garay ◽  
Olga Kalashnikova
Keyword(s):  
Machine Learning ◽  
Middle East ◽  
United Arab Emirates ◽  
Atmospheric Correction ◽  
Predictive Performance ◽  
Variable Importance ◽  
Learning Models ◽  
Median Test ◽  
Temporal And Spatial ◽  
Machine Learning Models

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2019 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
World Health ◽  
Support Vector ◽  
Strong Impact ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Depth Analysis ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s main pandemic vector-borne infections and serious hazard to humanity. According to the World Health Organization (WHO), the incidence of dengue has grown dramatically worldwide in recent decades. The WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. Until today there is no tested vaccine or treatment to stop or prevent dengue fever thus the importance of dengue outbreak prediction is significant. The current issue in dengue outbreak prediction is accuracy. There are a limited number of studies that look at in depth analysis of climate factors in dengue outbreak prediction. Methods: In this study, the most significant and important climatic factors that contribute to dengue outbreak were identified. These factors were used as input parameters on machine learning models. The models were trained and evaluated based on four-year data from January 2010 to December 2013 in Malaysia. Results: This work provides two main contributions. A new risk factor, which was called TempeRain Factor (TRF), was determined and used as an input parameter for dengue prediction outbreak model. Moreover, the TRF was applied to demonstrate that its strong impact on dengue outbreaks. Experimental results showed that Support Vector Machine (SVM) with the newly identified meteorological risk factor in this study resulted in higher accuracy of 98.09% and reduced the root mean square error to 0.098 for predicting dengue outbreak. Conclusions: This research managed to explore on the factors that are being used in dengue outbreak prediction systems. The main contribution of this paper is in identifying new significant factors that contribute in dengue outbreak prediction. From the evaluation, we managed to obtain a significant improvement in accuracy of the machine-learning model in dengue outbreak prediction.

scholarly journals Early Detection of Depression in Women using Machine Learning Methods

2019 ◽  
Vol 9 (2S) ◽  
pp. 352-358
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Age Groups ◽  
World Health Organisation ◽  
Scientific Discipline ◽  
World Health ◽  
Learning Models ◽  
Machine Learning Methods ◽  
Detection Of Depression ◽  
Machine Learning Models

According to World Health Organisation(WHO), most prevailing mental sickness and leading evidence of disability is Depression. In India Depression is much more prevalent in women of all age groups. Eventhough effectual treatment is noted for Depression, it is not reaching the maximum number of sufferers in both wealthy and pathetic countries. In this respect, many scientific discipline and researchers have been employed to develop Machine Learning models to determine level of Depression. This paper presents background knowledge on depression and useage of machine learning and also review past studies that apply machine learning for determine depression with their merits and demerits.

scholarly journals Forecasting under applying machine learning and statistical models

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 131-137
Author(s):  
Azhari Elhag ◽  
Hanaa Abu-Zinadah
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Real Life ◽  
World Health ◽  
Percentage Error ◽  
Learning Models ◽  
Kingdom Of Saudi Arabia ◽  
The World ◽  
Data Source ◽  
Health Organization

In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error.

Predicting the Linguistic Accessibility of Chinese Health Translations: Using Machine Learning Algorithms (Preprint)

2021 ◽  
Author(s):  
Christine Ji
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Health Resources ◽  
World Health Organisation ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Learning Models ◽  
The World ◽  
C5.0 Decision Tree ◽  
Machine Learning Models

BACKGROUND Linguistic accessibility has important impact on the reception and utilisation of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been under-studied. OBJECTIVE Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organisation health materials with original Chinese health resources developed by the Chinese health authorities. METHODS Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organisation with original Chinese materials from China Centre for Disease Control and Prevention. RESULTS Pairwise adjusted t test showed that three new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of AUC: C5.0 decision tree (p=0.000, 95% CI: -0.249, -0.152), random forest (p=0.000, 95% CI: 0.139, 0.239) and XGBoost Tree (p=0.000, 95% CI: 0.099, 0.193). There was however no significant difference between C5.0 decision tree and random forest (p=0.513). Extreme gradient boost tree was the best model having achieved statistically significant improvement over the C5.0 model (p=0.003) and the Random Forest model (p=0.006) at the adjusted Bonferroni p value at 0.008. CONCLUSIONS The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.

scholarly journals Determining hypertensive patients’ beliefs towards medication and associations with medication adherence using machine learning methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8286
Author(s):  
Firdaus Aziz ◽  
Sorayya Malek ◽  
Adliah Mhd Ali ◽  
Mee Sieng Wong ◽  
Mogeeb Mosleh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medication Adherence ◽  
Performance Measure ◽  
Variable Importance ◽  
Support Vector ◽  
Learning Models ◽  
Learning Methods ◽  
Hypertensive Patients ◽  
Machine Learning Methods ◽  
Machine Learning Models

Background This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients’ adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients’ adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature. Methods Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients’ adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients’ adherence levels and variables were generated using SOM. Result Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern. Conclusion This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients’ adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

Sign in / Sign up

Export Citation Format

Share Document