Special Issue "Machine Learning Applications in Public Health"

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: 31 August 2022.

Special Issue Editor

Dr. Quyen G. To
E-Mail Website
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 papers will be 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 semimonthly 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 2300 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 (2 papers)

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Research

Article
Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation
Int. J. Environ. Res. Public Health 2021, 18(18), 9873; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189873 - 19 Sep 2021
Viewed by 364
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 [...] Read more.
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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Article
Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic
Int. J. Environ. Res. Public Health 2021, 18(8), 4069; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18084069 - 12 Apr 2021
Viewed by 1441
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 [...] Read more.
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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