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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 "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 15617

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Special Issue Editors

Dr. Jeeyae Choi

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Guest Editor

College of Health and Human Services, University of North Carolina Wilmington, Wilmington, NC 28403, USA
Interests: clinical decision support systems; mobile apps for cancer patients; nursing information management system; knowledge management and representation

Dr. Albert Park

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Guest Editor

College of Computing and Informatic, The University of North Carolina Charlotte, Charlotte, NC 28223, USA
Interests: social computing; group-health informatics; human-centered design; personal (consumer) health informatics; online communication; collaborative health technology; patient-provider interaction; peer support; patient-reported outcomes and patient-generated data

Dr. Nansu Zong

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Guest Editor

Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
Interests: biomedical informatics; big data and linked data; machine learning; semantic web technologies

Special Issue Information

Dear Colleagues,

Rapid accumulation of various types of data involving human health now meets with cutting-edge technologies in data management and analytics. As we have recently seen from COVID-19 tracking tools and phenotyping algorithms, the analysis of these abundant data has been offering crucial insights into many health issues in various sectors. To recognize and share the latest efforts in utilizing various types of health data to answer difficult questions on human health, this Special Issue invites studies that report on a wide spectrum of topics related to health data use. Topics within the scope of this call include but are not limited to facilitating data reuse (e.g., data integration and harmonization), developing innovative methods for effectively managing and analyzing large amounts of data, drawing useful insights from analyzing data, and presenting data for effective communication. We welcome research with all types of health-related data, such as microbiology data, health survey data, and clinical data, and we are particularly interested in the works that utilize patient-generated health data.

Dr. Jeeyae Choi
Dr. Albert Park
Dr. Nansu Zong
Guest Editors

Manuscript Submission Information

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Keywords

Data reuse
Data-driven decision making
Predictive modeling
Data interoperability
Big data analytics
Patient generated health data
Patient-reported outcomes
Data visualization
mHealth date

Published Papers (5 papers)

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Research

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14 pages, 373 KiB

Open AccessArticle

Revisiting the Relationship between Altruism and Organ Donation: Insights from Israel

by Keren Dopelt, Lea Siton, Talya Harrison and Nadav Davidovitch

Int. J. Environ. Res. Public Health 2022, 19(12), 7404; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19127404 - 16 Jun 2022

Cited by 5 | Viewed by 2150

Abstract

The number of people on the waiting list for an organ transplant increases year after year. However, the number of donated organs available for transplantation does not rise in line with this increased demand. This study examines the associations between altruism, attitudes towards organ donation, and behavioral intentions regarding organ donation within the Jewish population in Israel. In a cross-sectional study, 452 participants completed an online questionnaire. Data collection occurred between November and December 2020. Convenience sampling was used, and participation was voluntary. Data were analyzed using Pearson correlations and independent samples t-tests. Within the study population, we found high levels of altruistic behaviors and positive attitudes toward organ donation. However, the level of behavioral intentions toward organ donation was low. No associations were found between altruism levels and attitudes toward organ donation, or between altruism levels and the degree of behavioral intentions toward organ donation. However, a positive relationship was found between attitudes toward organ donation and willingness to sign an organ donor card. In addition, positive associations were found between religiosity and altruism, while negative associations were found between religiosity and attitudes towards organ donation, and between religiosity and willingness to sign an organ donor card. Positive attitudes toward organ donation may result in increased organ donation in the future. Thus, raising awareness and positive attitudes toward organ donation among the wider public and, in particular, the ultra-Orthodox population in Israel in particular is necessary. Consequently, it is essential that information about the organ donation process is accessible and culturally adaptive to different sectors. Full article

(This article belongs to the Special Issue Health Data: Tools for Decision-Making)

12 pages, 26440 KiB

Open AccessArticle

Healthy vs. Unhealthy Food Images: Image Classification of Twitter Images

by Tejaswini Oduru, Alexis Jordan and Albert Park

Int. J. Environ. Res. Public Health 2022, 19(2), 923; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020923 - 14 Jan 2022

Cited by 5 | Viewed by 3694

Abstract

Obesity is a modern public health problem. Social media images can capture eating behavior and the potential implications to health, but research for identifying the healthiness level of the food image is relatively under-explored. This study presents a deep learning architecture that transfers features from a 152 residual layer network (ResNet) for predicting the level of healthiness of food images that were built using images from the Google images search engine gathered in 2020. Features learned from the ResNet 152 were transferred to a second network to train on the dataset. The trained SoftMax layer was stacked on top of the layers transferred from ResNet 152 to build our deep learning model. We then evaluate the performance of the model using Twitter images in order to better understand the generalizability of the methods. The results show that the model is able to predict the images into their respective classes, including Definitively Healthy, Healthy, Unhealthy and Definitively Unhealthy at an F1-score of 78.8%. This finding shows promising results for classifying social media images by healthiness, which could contribute to maintaining a balanced diet at the individual level and also understanding general food consumption trends of the public. Full article

(This article belongs to the Special Issue Health Data: Tools for Decision-Making)

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12 pages, 2517 KiB

Open AccessArticle

Exploring Impact of Marijuana (Cannabis) Abuse on Adults Using Machine Learning

by Jeeyae Choi, Joohyun Chung and Jeungok Choi

Int. J. Environ. Res. Public Health 2021, 18(19), 10357; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph181910357 - 01 Oct 2021

Cited by 6 | Viewed by 3137

Abstract

Marijuana is the most common illicit substance globally. The rate of marijuana use is increasing in young adults in the US. The current environment of legalizing marijuana use is further contributing to an increase of users. The purpose of this study was to explore the characteristics of adults who abuse marijuana (20–49 years old) and analyze behavior and social relation variables related to depression and suicide risk using machine-learning algorithms. A total of 698 participants were identified from the 2019 National Survey on Drug Use and Health survey as marijuana dependent in the previous year. Principal Component Analysis and Chi-square were used to select features (variables) and mean imputation method was applied for missing data. Logistic regression, Random Forest, and K-Nearest Neighbor machine-learning algorithms were used to build depression and suicide risk prediction models. The results showed unique characteristics of the group and well-performing prediction models with influential risk variables. Identified risk variables were aligned with previous studies and suggested the development of marijuana abuse prevention programs targeting 20–29 year olds with a regular depression and suicide screening. Further study is suggested for identifying specific barriers to receiving timely treatment for depression and suicide risk. Full article

(This article belongs to the Special Issue Health Data: Tools for Decision-Making)

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13 pages, 377 KiB

Open AccessArticle

ABO Blood Groups and the Incidence of Complications in COVID-19 Patients: A Population-Based Prospective Cohort Study

by Salvador Domènech-Montoliu, Joan Puig-Barberà, Maria Rosario Pac-Sa, Paula Vidal-Utrillas, Marta Latorre-Poveda, Alba Del Rio-González, Sara Ferrando-Rubert, Gema Ferrer-Abad, Manuel Sánchez-Urbano, Laura Aparisi-Esteve, Gema Badenes-Marques, Belén Cervera-Ferrer, Ursula Clerig-Arnau, Claudia Dols-Bernad, Maria Fontal-Carcel, Lorna Gomez-Lanas, David Jovani-Sales, Maria Carmen León-Domingo, Maria Dolores Llopico-Vilanova, Mercedes Moros-Blasco, Cristina Notari-Rodríguez, Raquel Ruíz-Puig, Sonia Valls-López and Alberto Arnedo-Pena Show full author list Hide full author list

Int. J. Environ. Res. Public Health 2021, 18(19), 10039; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph181910039 - 24 Sep 2021

Cited by 14 | Viewed by 2624

Abstract

After a COVID-19 outbreak in the Falles festival of Borriana (Spain) during March 2020, a cohort of patients were followed until October 2020 to estimate complications post-COVID-19, considering ABO blood groups (ABO). From 536 laboratory-confirmed cases, 483 completed the study (90.1%) carried by the Public Health Center of Castelló and the Emergency and Microbiology and Clinical Analysis of Hospital de la Plana Vila-real. The study included ABO determination and telephone interviews of patients. The participants had a mean age of 37.2 ± 17.1 years, 300 females (62.1%). ABO were O (41.4%), A (45.5%), B (9.1%), and AB (3.9%). We found no difference in the incidence of COVID-19 infections. A total of 159 (32.9%) patients reported one or more post-COVID-19 complications with divergent incidences after adjustment: O (32.3%), A (32.6%), B (54.1%), and AB (27.6%); B groups had more complications post-COVID-19 when compared with O group (adjusted relative risk [aRR] 95% confidence interval [CI] 1.68, 95% CI 1.24–2.27), and symptoms of fatigue (1.79, 95% CI 1.08–2.95), myalgia (2.06, 95% CI 1.10–3.84), headache (2.61, 95% CI 1.58–4.31), and disorder of vision (4.26 95% CI 1.33–13.60). In conclusion, we observed significant differences in post-COVID-19 complications by ABO, with a higher incidence in B group. Additional research is justified to confirm our results. Full article

(This article belongs to the Special Issue Health Data: Tools for Decision-Making)

Review

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23 pages, 1342 KiB

Open AccessReview

Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

by Adnan Muhammad Shah, Wazir Muhammad, Kangyoon Lee and Rizwan Ali Naqvi

Int. J. Environ. Res. Public Health 2021, 18(21), 11226; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182111226 - 26 Oct 2021

Cited by 11 | Viewed by 2994

Abstract

(1) Background: The appearance of physician rating websites (PRWs) has raised researchers’ interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers’ information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs’ features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor–patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making. Full article

(This article belongs to the Special Issue Health Data: Tools for Decision-Making)

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