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Applied Sciences
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New Trends in Medical Informatics II
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New Trends in Medical Informatics II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (10 September 2022) | Viewed by 10962
Related Special Issue: New Trends in Medical Informatics

Special Issue Editor

Prof. Dr. In Young Choi

E-Mail Website
Guest Editor
Department of Medical Informatics, The Catholic University of Korea, Seoul 06591, Korea
Interests: digital therapeutics; digital twin; mixed reality; augmented reality; Big Data; AI; medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applied Sciences is aiming to cover all of the latest state-of-the-art technological convergence in the field of medicine and healthcare. We thereby call for papers for this Special Issue that will describe recent research and developments in “New Trends in Medical Informatics”. We welcome expertise that is creative and interdisciplinary in a way that combines principles of new trends in technologies and medicine.

The main objective of this Special Issue is to present innovative research applying new 4th Industrial Revolution technology that is useful in medicine and healthcare. Remote telehealth-related systems and solutions have rapidly gained attention in the current post-coronavirus infectious disease era. Artificial intelligence is being applied to medicine to assist in all parts of medical practice from prevention to diagnosis to treatment. The blockchain algorithm is considered to be the next-era security technology fit for sensitive personal health data. Interdisciplinary original research covering any specific solutions and that tackles the above mentioned issues will be considered for this Special Issue.

Prof. Dr. In Young Choi
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. Applied Sciences 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 2400 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

  • Artificial Intelligence
  • blockchain
  • drone
  • 5G
  • 5G+
  • COVID-19
  • virus
  • telemedicine
  • remote healthcare
  • telehealth
  • biomedicine
  • materials
  • sensors
  • medical equipment
  • big data
  • 4th Industrial Revolution

Published Papers (4 papers)

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Research

13 pages, 3226 KiB  
Article
Establishment of the Optimal Common Data Model Environment for EMR Data Considering the Computing Resources of Medical Institutions
by Tong Min Kim, Taehoon Ko, Yoon-sik Yang, Sang Jun Park, In-Young Choi and Dong-Jin Chang
Appl. Sci. 2021, 11(24), 12056; https://doi.org/10.3390/app112412056 - 17 Dec 2021
Cited by 1 | Viewed by 2443
Abstract
Electronic medical record (EMR) data vary between institutions. These data should be converted into a common data model (CDM) for multi-institutional joint research. To build the CDM, it is essential to integrate the EMR data of each hospital and load it according to [...] Read more.
Electronic medical record (EMR) data vary between institutions. These data should be converted into a common data model (CDM) for multi-institutional joint research. To build the CDM, it is essential to integrate the EMR data of each hospital and load it according to the CDM model, considering the computing resources of each hospital. Accordingly, this study attempts to share experiences and recommend computing resource-allocation designs. Here, two types of servers were defined: combined and separated servers. In addition, three database (DB) setting types were selected: desktop application (DA), online transaction processing (OLTP), and data warehouse (DW). Scale, TPS, average latency, 90th percentile latency, and maximum latency were compared across various settings. Virtual memory (vmstat) and disk input/output (disk) statuses were also described. Transactions per second (TPS) decreased as the scale increased in all DB types; however, the average, 90th percentile and maximum latencies exhibited no tendency according to scale. When compared with the maximum number of clients (DA client = 5, OLTP clients = 20, DW clients = 10), the TPS, average latency, 90th percentile latency, and maximum latency values were highest in the order of OLTP, DW, and DA. In vmstat, the amount of memory used for the page cache field and free memory currently available for DA, OLTP, and DW were large compared to other fields. In the disk, DA, OLTP, and DW all recorded the largest value in the average size of write requests, followed by the largest number of write requests per second. In summary, this study presents recommendations for configuring CDM settings. The configuration must be tuned carefully, considering the hospital’s resources and environment, and the size of the database must consider concurrent client connections, architecture, and connections. Full article
(This article belongs to the Special Issue New Trends in Medical Informatics II)
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13 pages, 3220 KiB  
Article
Common Data Model and Database System Development for the Korea Biobank Network
by Soo-Jeong Ko, Wona Choi, Ki-Hoon Kim, Seo-Joon Lee, Haesook Min, Seol-Whan Oh and In Young Choi
Appl. Sci. 2021, 11(24), 11825; https://doi.org/10.3390/app112411825 - 13 Dec 2021
Cited by 3 | Viewed by 3167
Abstract
The importance of clinical information related to specimens is increasing due to the research on human biological specifications being conducted worldwide. In order to utilize data, it is necessary to define the range of data and develop a standardized system for collected resources. [...] Read more.
The importance of clinical information related to specimens is increasing due to the research on human biological specifications being conducted worldwide. In order to utilize data, it is necessary to define the range of data and develop a standardized system for collected resources. The purpose of this study is to establish clinical information standardization and to allow clinical information management systems to improve the utilization of biological specifications. The KBN CDM, consisting of 18 tables and 177 variables, was developed. The clinical information codes were mapped in standard terms. The 27 diseases in the group were collected from 17 biobanks, and all disorders not belonging to the group were standardized and loaded. We also developed a system that provides statistical visualization screens and data retrieval tools for data collection. This study developed a unified management system to model KBN CDM that collects standardized data, manages clinical information, and shares the information systematically. Through this system, all participating biobanks can be integrated into one system for integrated management and research. Full article
(This article belongs to the Special Issue New Trends in Medical Informatics II)
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20 pages, 61871 KiB  
Article
Design Process for a Birthing Bed, Based on User Hierarchy: Promoting Improvement in User Satisfaction
by Fabiola Cortes-Chavez, Alberto Rossa-Sierra and Elvia Luz Gonzalez-Muñoz
Appl. Sci. 2021, 11(20), 9430; https://doi.org/10.3390/app11209430 - 11 Oct 2021
Cited by 1 | Viewed by 2362
Abstract
The medical device design process has a responsibility to define the characteristics of the object to ensure its correct interaction with users. This study presents a proposal to improve medical device design processes in order to increase user acceptance by considering two key [...] Read more.
The medical device design process has a responsibility to define the characteristics of the object to ensure its correct interaction with users. This study presents a proposal to improve medical device design processes in order to increase user acceptance by considering two key factors: the user hierarchy and the relationship with the patient’s health status. The goal of this study is to address this research gap and to increase design factors with practical suggestions for the design of new medical devices. The results obtained here will help medical device designers make more informed decisions about the functions and features required in the final product during the development stage. In addition, we aim to help researchers with design process didactics that demonstrate the importance of the correct execution of the process and how the factors considered can have an impact on the final product. An experiment was conducted with 40 design engineering students who designed birthing beds via two design processes: the traditional product design process and the new design process based on hierarchies (proposed in this study). The results showed a significant increase in the user acceptance of the new birthing bed developed with the hierarchical-based design process. Full article
(This article belongs to the Special Issue New Trends in Medical Informatics II)
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12 pages, 677 KiB  
Article
Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
by Ki-Hoon Kim, Wona Choi, Soo-Jeong Ko, Dong-Jin Chang, Yeon-Woog Chung, Se-Hyun Chang, Jae-Kwon Kim, Dai-Jin Kim and In-Young Choi
Appl. Sci. 2021, 11(19), 9188; https://doi.org/10.3390/app11199188 - 2 Oct 2021
Cited by 3 | Viewed by 2334
Abstract
Healthcare data has economic value and is evaluated as such. Therefore, it attracted global attention from observational and clinical studies alike. Recently, the importance of data quality research emerged in healthcare data research. Various studies are being conducted on this topic. In this [...] Read more.
Healthcare data has economic value and is evaluated as such. Therefore, it attracted global attention from observational and clinical studies alike. Recently, the importance of data quality research emerged in healthcare data research. Various studies are being conducted on this topic. In this study, we propose a DQ4HEALTH model that can be applied to healthcare when reviewing existing data quality literature. The model includes 5 dimensions and 415 validation rules. The four evaluation indicators include the net pass rate (NPR), weighted pass rate (WPR), net dimensional pass rate (NDPR), and weighted dimensional pass rate (WDPR). They were used to evaluate the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) at three medical institutions. These indicators identify differences in data quality between the institutions. The NPRs of the three institutions (A, B, and C) were 96.58%, 90.08%, and 90.87%, respectively, and the WPR was 98.52%, 94.26%, and 94.81%, respectively. In the quality evaluation of the dimensions, the consistency was 70.06% of the total error data. The WDPRs were 98.22%, 94.74%, and 95.05% for institutions A, B, and C, respectively. This study presented indices for comparing quality evaluation models and quality in the healthcare field. Using these indices, medical institutions can evaluate the quality of their data and suggest practical directions for decreasing errors. Full article
(This article belongs to the Special Issue New Trends in Medical Informatics II)
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