Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
Journals
IJERPH
Special Issues
Bridging Healthcare Operations Management and Information and Communication Technology (ICT)...
ijerph-logo
Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Bridging Healthcare Operations Management and Information and Communication Technology (ICT) in the Post-pandemic Era

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 7010

Special Issue Editors

Dr. Hainan Guo

E-Mail Website
Guest Editor
College of Management, Shenzhen University, Shenzhen 518060, China
Interests: optimal allocation of medical resources; hierarchical diagnosis and treatment; telemedicine; diagnosis and treatment decision optimization; medical big data analysis; hospital logistics; smart pension
Prof. Dr. Gang Du

E-Mail Website
Guest Editor
Faculty of Economics and Management, East China Normal University, Shanghai 200062, China
Interests: scheduling optimization of medical resources; medical resource allocation for online and offline; diagnosis and treatment decision optimization; big data analytics for healthcare management; operation management for online health community; home health care management; smart medicine
Dr. Yang Zhao

E-Mail Website
Guest Editor
School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
Interests: applied statistics; data mining; intelligent health care; medical informatics; public health surveillance

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has reshaped our lives in an unprecedented way all around the world, posing critical challenges but also creating a huge opportunity in the field of healthcare. With the rapid development of information technology and computation capability, the pandemic has fueled the boom of many new health services, such as telehealth, digital smart medicine, IoT healthcare, home healthcare and so on. The increasing availability of healthcare relevant data makes possible better disease prevention, diagnostic and healthcare system operations. In addition, many innovative data-driven approaches have been developed for a wide range of healthcare applications such as telemedical consultation center planning, healthcare informatics, health monitoring and management system, home healthcare planning, healthcare delivery optimization, and medical staff scheduling. This Special Issue seeks to bridge healthcare and ICT to help to develop better operation strategies, which can further improve the quality and efficiency of healthcare services. In other words, this Special Issue aims to provide a comprehensive view of health services improvement with ICT among various research topics such as optimization, scheduling, forecasting, recommendation and so on. Potential topics include, but are not limited to: l Interdisciplinary cooperation optimization in telehealth; l Healthcare delivery optimization in the context of smart medicine; l Data-driven predictive analysis for precision medicine; l Machine Learning techniques of big data analytics for disease prevention and diagnostic l AI-supported techniques for healthcare recommendation system; l IoT healthcare analysis l Digital health technologies l The scheduling and routing optimization in home healthcare system; l Smart technologies for improving the quality of mobile health care.

Dr. Hainan Guo
Prof. Dr. Gang Du
Dr. Yang Zhao
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

  • healthcare operations management
  • healthcare informatics
  • smart medicine
  • telehealth
  • home healthcare
  • IoT healthcare

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 789 KiB  
Article
Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review
by Yao Cai, Fei Yu, Manish Kumar, Roderick Gladney and Javed Mostafa
Int. J. Environ. Res. Public Health 2022, 19(22), 15115; https://doi.org/10.3390/ijerph192215115 - 16 Nov 2022
Cited by 4 | Viewed by 2521
Abstract
A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, [...] Read more.
A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management. Full article
(This article belongs to the Special Issue Bridging Healthcare Operations Management and Information and Communication Technology (ICT) in the Post-pandemic Era)
►▼ Show Figures

Figure 1

16 pages, 1054 KiB  
Article
Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type
by Yifan Qin, Jinlong Wu, Wen Xiao, Kun Wang, Anbing Huang, Bowen Liu, Jingxuan Yu, Chuhao Li, Fengyu Yu and Zhanbing Ren
Int. J. Environ. Res. Public Health 2022, 19(22), 15027; https://doi.org/10.3390/ijerph192215027 - 15 Nov 2022
Cited by 12 | Viewed by 3899
Abstract
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle [...] Read more.
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999–2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients. Full article
(This article belongs to the Special Issue Bridging Healthcare Operations Management and Information and Communication Technology (ICT) in the Post-pandemic Era)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop