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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 16162

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Special Issue Editor

Dr. Quyen G. To

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Guest Editor

1. School of Health, Medical and Applied Sciences, Central Queensland University, Bruce Highway, Rockhampton, Queensland 4702, Australia 2. Physical Activity Research Group, Appleton Institute, Central Queensland University, 44 Greenhill Road, Wayville 504, Australia
Interests: physical activity; chronic diseases; machine learning; digital health

Special Issue Information

Dear Colleagues,

We are organizing a Special Issue on Machine Learning Applications in Health Research in the Journal of Environmental Research and Public Health. This is a peer-reviewed journal that publishes articles in the interdisciplinary area of environmental health sciences and public health. For detailed information on the journal, I refer you to https://0-www-mdpi-com.brum.beds.ac.uk/journal/ijerph.

Artificial Intelligence and Machine Learning are transforming human lives with numerous real-world applications in medical imaging diagnosis, drug discovery, personalized medicine, and behavioral change. As the field is rapidly expanding, this Special Issue is a venue for communicating findings from high-quality and innovative research that applies machine learning techniques to improve human health and behaviors. Authors are invited to submit original manuscripts focusing on, but not limited to, tool development, measurement, evaluation, and technology-based solutions that address contemporary public health issues. The listed keywords can be used as a guide; other related topics will also be considered.

Dr. Quyen G. To
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

physical activity
exercise
fitness
sleep
nutrition
wearables
activity tracker
accelerometer
artificial intelligence
machine learning
deep learning
neural network
natural language processing
image recognition

Published Papers (6 papers)

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Research

19 pages, 5545 KiB

Open AccessArticle

Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach

by Frank Li, Jiwoong Choi, Xuan Zhang, Prathish K. Rajaraman, Chang-Hyun Lee, Hongseok Ko, Kum-Ju Chae, Eun-Kee Park, Alejandro P. Comellas, Eric A. Hoffman and Ching-Long Lin

Int. J. Environ. Res. Public Health 2022, 19(19), 11894; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191911894 - 20 Sep 2022

Viewed by 1380

Abstract

Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HDLI). However, many people who have claimed that they experienced HD exposure were not diagnosed with HDLI but still felt discomfort, possibly due to the unknown effects of HD. Therefore, this study examined HD-exposed subjects with normal-appearing lungs, as well as unexposed subjects, in clusters (subgroups) with distinct characteristics, classified by deep-learning-derived computed-tomography (CT)-based tissue pattern latent traits. Among the major clusters, cluster 0 (C0) and cluster 5 (C5) were dominated by HD-exposed and unexposed subjects, respectively. C0 was characterized by features attributable to lung inflammation or fibrosis in contrast with C5. The computational fluid and particle dynamics (CFPD) analysis suggested that the smaller airway sizes observed in the C0 subjects led to greater airway resistance and particle deposition in the airways. Accordingly, women appeared more vulnerable to HD-associated lung abnormalities than men. Full article

(This article belongs to the Special Issue Machine Learning Applications in Public Health)

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17 pages, 2865 KiB

Open AccessArticle

An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data

by Rosy Oh, Hong Kyu Lee, Youngmi Kim Pak and Man-Suk Oh

Int. J. Environ. Res. Public Health 2022, 19(10), 5800; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19105800 - 10 May 2022

Cited by 3 | Viewed by 1871

Abstract

The early prediction and identification of risk factors for diabetes may prevent or delay diabetes progression. In this study, we developed an interactive online application that provides the predictive probabilities of prediabetes and diabetes in 4 years based on a Bayesian network (BN) classifier, which is an interpretable machine learning technique. The BN was trained using a dataset from the Ansung cohort of the Korean Genome and Epidemiological Study (KoGES) in 2008, with a follow-up in 2012. The dataset contained not only traditional risk factors (current diabetes status, sex, age, etc.) for future diabetes, but it also contained serum biomarkers, which quantified the individual level of exposure to environment-polluting chemicals (EPC). Based on accuracy and the area under the curve (AUC), a tree-augmented BN with 11 variables derived from feature selection was used as our prediction model. The online application that implemented our BN prediction system provided a tool that performs customized diabetes prediction and allows users to simulate the effects of controlling risk factors for the future development of diabetes. The prediction results of our method demonstrated that the EPC biomarkers had interactive effects on diabetes progression and that the use of the EPC biomarkers contributed to a substantial improvement in prediction performance. Full article

(This article belongs to the Special Issue Machine Learning Applications in Public Health)

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15 pages, 475 KiB

Open AccessArticle

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers

by Meng Ji, Wenxiu Xie, Riliu Huang and Xiaobo Qian

Int. J. Environ. Res. Public Health 2021, 18(20), 10743; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182010743 - 13 Oct 2021

Viewed by 1367

Abstract

We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental health information of high actionability for people with mental disorders. sensitivity of the classifier using both semantic and structural features as variables achieved statistically higher than that of the binary classifier using either semantic (p < 0.001) or structural features (p = 0.0010). The specificity of the optimized classifier was statistically higher than that of the classifier using structural variables (p = 0.002) and the classifier using semantic variables (p = 0.001). Differences in specificity between the full-variable classifier and the optimised classifier were statistically insignificant (p = 0.687). These findings suggest the optimised classifier using as few as 19 semantic-structural variables was the best-performing classifier. By combining insights of linguistics and statistical analyses, we effectively increased the interpretability and the diagnostic utility of the binary classifiers to guide the development, evaluation of the actionability and usability of mental healthcare information. Full article

(This article belongs to the Special Issue Machine Learning Applications in Public Health)

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14 pages, 1513 KiB

Open AccessArticle

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

by Wachiranun Sirikul, Nida Buawangpong, Ratana Sapbamrer and Penprapa Siviroj

Int. J. Environ. Res. Public Health 2021, 18(19), 10540; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph181910540 - 08 Oct 2021

Cited by 4 | Viewed by 2218

Abstract

Background: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Methods: Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002–2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Results: Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Conclusions: Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation. Full article

(This article belongs to the Special Issue Machine Learning Applications in Public Health)

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11 pages, 1100 KiB

Open AccessArticle

Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation

by Meng Ji, Wenxiu Xie, Riliu Huang and Xiaobo Qian

Int. J. Environ. Res. Public Health 2021, 18(18), 9873; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189873 - 19 Sep 2021

Cited by 2 | Viewed by 1771

Abstract

Background: Machine translation (MT) technologies have increasing applications in healthcare. Despite their convenience, cost-effectiveness, and constantly improved accuracy, research shows that the use of MT tools in medical or healthcare settings poses risks to vulnerable populations. Objectives: We aimed to develop machine learning classifiers (MNB and RVM) to forecast nuanced yet significant MT errors of clinical symptoms in Chinese neural MT outputs. Methods: We screened human translations of MSD Manuals for information on self-diagnosis of infectious diseases and produced their matching neural MT outputs for subsequent pairwise quality assessment by trained bilingual health researchers. Different feature optimisation and normalisation techniques were used to identify the best feature set. Results: The RVM classifier using optimised, normalised (L₂ normalisation) semantic features achieved the highest sensitivity, specificity, AUC, and accuracy. MNB achieved similar high performance using the same optimised semantic feature set. The best probability threshold of the best performing RVM classifier was found at 0.6, with a very high positive likelihood ratio (LR+) of 27.82 (95% CI: 3.99, 193.76), and a low negative likelihood ratio (LR−) of 0.19 (95% CI: 0.08, 046), suggesting the high diagnostic utility of our model to predict the probabilities of erroneous MT of disease symptoms to help reverse potential inaccurate self-diagnosis of diseases among vulnerable people without adequate medical knowledge or an ability to ascertain the reliability of MT outputs. Conclusion: Our study demonstrated the viability, flexibility, and efficiency of introducing machine learning models to help promote risk-aware use of MT technologies to achieve optimal, safer digital health outcomes for vulnerable people. Full article

(This article belongs to the Special Issue Machine Learning Applications in Public Health)

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9 pages, 292 KiB

Open AccessArticle

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

by Quyen G. To, Kien G. To, Van-Anh N. Huynh, Nhung T. Q. Nguyen, Diep T. N. Ngo, Stephanie J. Alley, Anh N. Q. Tran, Anh N. P. Tran, Ngan T. T. Pham, Thanh X. Bui and Corneel Vandelanotte

Int. J. Environ. Res. Public Health 2021, 18(8), 4069; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18084069 - 12 Apr 2021

Cited by 35 | Viewed by 5270

Abstract

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies. Full article

(This article belongs to the Special Issue Machine Learning Applications in Public Health)

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