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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 "Digital Health".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 17677

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Special Issue Editors

Dr. María Teresa García Ordás

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Guest Editor

Seguridad y Conocimiento en el Mundo Cibernético (SECOMUCI) Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, C.P. 24071 León, Spain
Interests: artificial intelligence; neural network; deep learning; machine learning; eHealth

Dr. José Alberto Benítez Andrades

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Salud Bienestar Ingeniería y Sostenibilidad Sociosanitaria (SALBIS) Research Group, Department of Electric, Systems and Automatics Engineering, University of León, Campus of Vegazana s/n, 24071 León, Spain
Interests: knowledge engineering; ontologies; artificial intelligence; machine learning; natural language processing; knowledge graphs; eHealth; public health
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Dr. Jose Luis Calvo-Rolle

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Guest Editor

Department of Industrial Engineering, University of A Coruña, 15405 Ferrol, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modeling; optimization, and control; fault and anomaly detection using traditional and intelligent techniques; new sensors; robust sensors; and virtual sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence and Knowledge Engineering in Public Health papers must relate to real-world medical domains. Technical and medical points of view must be presented at the proper depth. This Special Issue is looking for innovative works in the artificial intelligence and knowledge engineering fields, which must have a special relevance and a clear application to public health.

Related to artificial intelligence, supervised or unsupervised learning techniques for dealing with medical data, robotic systems improvements or new AI systems using the Internet of Medical Things (IoMT), such as medical or wearable devices, sensors or apps, and data analytics for public health care, are in the scope of this Special Issue.

On the other hand, the scope and topics of interest related to knowledge engineering are listed below: ontologies and controlled vocabularies, linked data, natural language processing and semantic web, human–computer interaction, knowledge representation and reasoning, storage solutions for the semantic web, semantic technologies in medicine and semantic social web.

All papers must be methodologically sound, showing how the proposal can be applied to real public health scenarios, and they have to explicitly mention improvements or advantages with respect to existing approaches and offer a discussion stressing the novelty of the work.

Including publicly available datasets is strongly recommended to increase the reproducibility of the research works.

Dr. María Teresa García Ordás
Dr. José Alberto Benítez Andrades
Dr. Jose Luis Calvo-Rolle
Guest Editors

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

deep learning
machine learning
internet of medical things
AI for public heath
knowledge engineering
knowledge representation
ontologies in public health
semantic web
semantic technologies

Published Papers (6 papers)

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Research

16 pages, 1034 KiB

Open AccessArticle

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

by Abdulrahman A. Alrajhi, Osama A. Alswailem, Ghassan Wali, Khalid Alnafee, Sarah AlGhamdi, Jhan Alarifi, Sarab AlMuhaideb, Hisham ElMoaqet and Ahmad AbuSalah

Int. J. Environ. Res. Public Health 2022, 19(5), 2958; https://doi.org/10.3390/ijerph19052958 - 03 Mar 2022

Cited by 5 | Viewed by 1959

Abstract

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (

n = 462

patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (

n = 185

patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Public Health)

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20 pages, 1104 KiB

Open AccessArticle

Adopting Artificial Intelligence in Public Healthcare: The Effect of Social Power and Learning Algorithms

by Tara Qian Sun

Int. J. Environ. Res. Public Health 2021, 18(23), 12682; https://doi.org/10.3390/ijerph182312682 - 01 Dec 2021

Cited by 8 | Viewed by 2688

Abstract

Although the use of artificial intelligence (AI) in healthcare is still in its early stages, it is important to understand the factors influencing its adoption. Using a qualitative multi-case study of three hospitals in China, we explored the research of factors affecting AI adoption from a social power perspective with consideration of the learning algorithm abilities of AI systems. Data were collected through semi-structured interviews, participative observations, and document analysis, and analyzed using NVivo 11. We classified six social powers into knowledge-based and non-knowledge-based power structures, revealing a social power pattern related to the learning algorithm ability of AI. Full article

(This article belongs to the Special Issue Artificial Intelligence in Public Health)

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17 pages, 969 KiB

Open AccessArticle

Executive Functioning in Adults with Down Syndrome: Machine-Learning-Based Prediction of Inhibitory Capacity

by Mario Fernando Jojoa-Acosta, Sara Signo-Miguel, Maria Begoña Garcia-Zapirain, Mercè Gimeno-Santos, Amaia Méndez-Zorrilla, Chandan J. Vaidya, Marta Molins-Sauri, Myriam Guerra-Balic and Olga Bruna-Rabassa

Int. J. Environ. Res. Public Health 2021, 18(20), 10785; https://doi.org/10.3390/ijerph182010785 - 14 Oct 2021

Cited by 11 | Viewed by 2063

Abstract

The study of executive function decline in adults with Down syndrome (DS) is important, because it supports independent functioning in real-world settings. Inhibitory control is posited to be essential for self-regulation and adaptation to daily life activities. However, cognitive domains that most predict the capacity for inhibition in adults with DS have not been identified. The aim of this study was to identify cognitive domains that predict the capacity for inhibition, using novel data-driven techniques in a sample of adults with DS (n = 188; 49.47% men; 33.6 ± 8.8 years old), with low and moderate levels of intellectual disability. Neuropsychological tests, including assessment of memory, attention, language, executive functions, and praxis, were submitted to Random Forest, support vector machine, and logistic regression algorithms for the purpose of predicting inhibition capacity, assessed with the Cats-and-Dogs test. Convergent results from the three algorithms show that the best predictors for inhibition capacity were constructive praxis, verbal memory, immediate memory, planning, and written verbal comprehension. These results suggest the minimum set of neuropsychological assessments and potential intervention targets for individuals with DS and ID, which may optimize potential for independent living. Full article

(This article belongs to the Special Issue Artificial Intelligence in Public Health)

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32 pages, 1497 KiB

Open AccessArticle

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

by Oscar Jossa-Bastidas, Sofia Zahia, Andrea Fuente-Vidal, Néstor Sánchez Férez, Oriol Roda Noguera, Joel Montane and Begonya Garcia-Zapirain

Int. J. Environ. Res. Public Health 2021, 18(20), 10769; https://doi.org/10.3390/ijerph182010769 - 14 Oct 2021

Cited by 11 | Viewed by 4465

Abstract

The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition. Full article

(This article belongs to the Special Issue Artificial Intelligence in Public Health)

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28 pages, 3778 KiB

Open AccessArticle

Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

by Nahúm Cueto López, María Teresa García-Ordás, Facundo Vitelli-Storelli, Pablo Fernández-Navarro, Camilo Palazuelos and Rocío Alaiz-Rodríguez

Int. J. Environ. Res. Public Health 2021, 18(20), 10670; https://doi.org/10.3390/ijerph182010670 - 12 Oct 2021

Cited by 10 | Viewed by 2296

Abstract

This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest. Full article

(This article belongs to the Special Issue Artificial Intelligence in Public Health)

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14 pages, 17777 KiB

Open AccessArticle

Residual Self-Calibration and Self-Attention Aggregation Network for Crop Disease Recognition

by Qiang Zhang, Banyong Sun, Yaxiong Cheng and Xijie Li

Int. J. Environ. Res. Public Health 2021, 18(16), 8404; https://doi.org/10.3390/ijerph18168404 - 09 Aug 2021

Cited by 4 | Viewed by 1692

Abstract

The correct diagnosis and recognition of crop diseases play an important role in ensuring crop yields and preventing food safety. The existing methods for crop disease recognition mainly focus on accuracy while ignoring the algorithm’s robustness. In practice, the acquired images are often accompanied by various noises. These noises lead to a huge challenge for improving the robustness and accuracy of the recognition algorithm. In order to solve this problem, this paper proposes a residual self-calibration and self-attention aggregation network (RCAA-Net) for crop disease recognition in actual scenarios. The proposed RCAA-Net is composed of three main modules: (1) multi-scale residual module, (2) feedback self-calibration module, and (3) self-attention aggregation module. Specifically, the multi-scale residual module is designed to learn multi-scale features and provide both global and local information for the appearance of the disease to improve the performance of the model. The feedback self-calibration is proposed to improve the robustness of the model by suppressing the background noise in the original deep features. The self-attention aggregation module is introduced to further improve the robustness and accuracy of the model by capturing multi-scale information in different semantic spaces. The experimental results on the challenging 2018ai_challenger crop disease recognition dataset show that the proposed RCAA-Net achieves state-of-the-art performance on robustness and accuracy for crop disease recognition in actual scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence in Public Health)

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