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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 July 2021) | Viewed by 37335

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Special Issue Editor

Prof. Dr. In Young Choi

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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 of this Special Issue which will describe recent research and developments in New Trends in Medical Informatics. We welcome expertise that are creative and interdisciplinary in a way that combines principles of new trend technologies and medicine.

Main objective of this Special Issue is to present innovative research applying new 4th industrial revolution technology useful in medicine and healthcare. Remote tele-health related systems and solutions have rapidly gained attention in the current post-Corona virus infectious disease era. Artificial intelligence is being applied to medicine to assist all parts of medical practice from prevention, diagnosis to treatment. Blockchain algorithm is considered as the next era security technology fit for sensitive personal health data. Interdisciplinary original researches underlying any specific solutions that tackles issues mentioned above will be considered suitable for this Special Issue.

Prof. Dr. In Young Choi
Guest Editor

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Keywords

Artificial Intelligence
Blockchain
Drone
5G
5G+
Virus
Telemedicine
Remote Healthcare
Telehealth
Biomedicine
Materials
Sensors
Medical equipment
Big Data
4th Industrial Revolution

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Published Papers (11 papers)

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Research

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13 pages, 9580 KiB

Open AccessArticle

s-Guard: Multisensor Embedded Obstructive Sleep Apnea and Bruxism Real-Time Data Transmission Intraoral Appliance Device

by Seo-Joon Lee, Il-Do Jeong, Eo-Bin Kim, Jin-Young Park, In-Hwan Jo, Jae-Hoon Han and Tae-Young Jung

Appl. Sci. 2021, 11(9), 4182; https://doi.org/10.3390/app11094182 - 4 May 2021

Cited by 4 | Viewed by 2626

Abstract

Obstructive sleep apnea (OSA) and bruxism are widely recognized as common forms of sleep obstruction in modern everyday life. The most representative and conventional treatment method using continuous positive airway pressure has a critical problem owing to its high inconvenience. A relatively modern alternative solution is the mandibular advancement device, but it still has no monitoring function for patient compliance. Therefore, this research proposes Sleep Guard (s-Guard), a multisensor embedded OSA monitoring intraoral appliance device based on Internet-of-Things technology. Relevant health information monitoring sensors, such as temperature, gyroscope, accelerometer, and SpO2 sensors, were embedded for real-time health monitoring. Results showed an average transmission speed of 91,870.19 bytes per second, a successful connection check rate of 100%, and a wireless data stream error rate of 0.1%. Overall, the actual speed, connection, and error test results revealed the robust functioning of s-Guard in real monitoring scenarios. This research is envisioned to greatly enhance patient compliance when treating OSA or bruxism and is also expected to motivate other sensors to be embedded in our proposed model for the application of other disease areas. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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9 pages, 1381 KiB

Open AccessArticle

Filtered BERT: Similarity Filter-Based Augmentation with Bidirectional Transfer Learning for Protected Health Information Prediction in Clinical Documents

by Min Kang, Kye Hwa Lee and Youngho Lee

Appl. Sci. 2021, 11(8), 3668; https://doi.org/10.3390/app11083668 - 19 Apr 2021

Cited by 7 | Viewed by 3601

Abstract

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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17 pages, 3061 KiB

Open AccessArticle

LabConcept—A New Mobile Healthcare Platform for Standardizing Patient Results in Telemedicine

by Manole-Stefan Niculescu, Adriana Florescu and Sever Pasca

Appl. Sci. 2021, 11(4), 1935; https://doi.org/10.3390/app11041935 - 23 Feb 2021

Cited by 2 | Viewed by 2793

Abstract

The aim of this paper is to present a new original software platform, called LabConcept, that is designed for monitoring health activity in order to establish a more efficient treatment plan and provide a better lifestyle via telemedicine. It is fully designed by the authors and embeds all the patient’s healthcare information into a mobile software application, compatible with smartphones, laptops or other mobile devices. The novelty consists in standardizing the laboratory and the point-of-care results into a Quick Response (QR) code that is printed on every result bulletin issued and given to the patient after his routine blood assays are performed. Compared to the existing telemedicine software (VSee, Teladoc, TheraPlatform, Chiron Health), which have the basic videoconferencing role, LabConcept has a set of new important features that start from the concept of telemedicine, designed to load and create databases for all the assayed results in order to be sent to the doctor’s LabConcept application for professional interpretation. The software also analyzes the clinical evolution of an assay on a user-predefined period of time, automatically pre-diagnoses and alerts the user about a potential disorder from the stored results and records comments regarding the efficiency of the patient’s treatment plan. LabConcept is an easy and safe end-to-end real-time patient–doctor communication channel, conducive to improved monitoring and treatment procedures for the patient. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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13 pages, 3206 KiB

Open AccessArticle

CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning

by Tong Min Kim, Seo-Joon Lee, Hwa Young Lee, Dong-Jin Chang, Chang Ii Yoon, In-Young Choi and Kun-Ho Yoon

Appl. Sci. 2020, 10(23), 8575; https://doi.org/10.3390/app10238575 - 30 Nov 2020

Cited by 3 | Viewed by 2370

Abstract

The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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36 pages, 10786 KiB

Open AccessArticle

MobiDiabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2DM)

by Anakaren Alcala Juárez, Manuel Alejandro Hernández Villegas, Eduardo López Domínguez, Yesenia Hernández Velázquez, Saúl Domínguez Isidro, Liliana Ovando Diego, Olivia Hernández Sánchez, Jorge De La Calleja, Saúl Eduardo Pomares Hernández and María Auxilio Medina Nieto

Appl. Sci. 2020, 10(22), 8036; https://doi.org/10.3390/app10228036 - 12 Nov 2020

Cited by 2 | Viewed by 2484

Abstract

According to the International Diabetes Federation, in 2019, approximately 416.7 million people worldwide suffered from type 2 diabetes mellitus (T2DM). T2DM is a chronic degenerative disease of long term and slow progression. This condition requires a strict follow-up by physicians and nutritionists, combined with rigorous adherence to treatment by the patient to avoid possible complications. In this context, this paper describes the analysis, design, development, and preliminary usability assessment of a telemonitoring system focused on the monitoring, control, and remote nutritional therapy of people with T2DM. The proposed system comprises two mobile web applications, one focused on the patient and another oriented to physicians and nutritionists. The central services that our system provides to the patient and health personnel are: generate risk alerts; consult food menu options; receive recommendations; consult results of the food intake frequency questionnaire; patient history management; record anthropometry of patients, and review health education material. We carry out a preliminary usability assessment of our system based on a field study with four physicians, two nutritionists, and seven patients with T2DM. Based on the obtained results, our telemonitoring system shows a satisfactory/favorable opinion in terms of usability from the users’ perspective. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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11 pages, 751 KiB

Open AccessArticle

MARIE: A Context-Aware Term Mapping with String Matching and Embedding Vectors

by Han Kyul Kim, Sae Won Choi, Ye Seul Bae, Jiin Choi, Hyein Kwon, Christine P. Lee, Hae-Young Lee and Taehoon Ko

Appl. Sci. 2020, 10(21), 7831; https://doi.org/10.3390/app10217831 - 4 Nov 2020

Cited by 4 | Viewed by 2485

Abstract

With growing interest in machine learning, text standardization is becoming an increasingly important aspect of data pre-processing within biomedical communities. As performances of machine learning algorithms are affected by both the amount and the quality of their training data, effective data standardization is needed to guarantee consistent data integrity. Furthermore, biomedical organizations, depending on their geographical locations or affiliations, rely on different sets of text standardization in practice. To facilitate easier machine learning-related collaborations between these organizations, an effective yet practical text data standardization method is needed. In this paper, we introduce MARIE (a context-aware term mapping method with string matching and embedding vectors), an unsupervised learning-based tool, to find standardized clinical terminologies for queries, such as a hospital’s own codes. By incorporating both string matching methods and term embedding vectors generated by BioBERT (bidirectional encoder representations from transformers for biomedical text mining), it utilizes both structural and contextual information to calculate similarity measures between source and target terms. Compared to previous term mapping methods, MARIE shows improved mapping accuracy. Furthermore, it can be easily expanded to incorporate any string matching or term embedding methods. Without requiring any additional model training, it is not only effective, but also a practical term mapping method for text data standardization and pre-processing. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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13 pages, 2605 KiB

Open AccessArticle

Development of a Mobile Personal Health Record Application Designed for Emergency Care in Korea; Integrated Information from Multicenter Electronic Medical Records

by Yuri Choi, June-sung Kim, In Ho Kwon, Taerim Kim, Su Min Kim, Wonchul Cha, Jinwoo Jeong and Jae-Ho Lee

Appl. Sci. 2020, 10(19), 6711; https://doi.org/10.3390/app10196711 - 25 Sep 2020

Cited by 10 | Viewed by 5038

Abstract

Collecting patient’s medical data is essential for emergency care. Although hospital-tethered personal health records (PHRs) can provide accurate data, they are not available as electronic information when the hospital does not develop and supply PHRs. The objective of this research was to evaluate whether a mobile app can assemble health data from different hospitals and enable interoperability. Moreover, we identified numerous barriers to overcome for putting health data into one place. The new mobile PHR (mPHR) application was developed and evaluated according to the four phases of the system development life cycle: defining input data and functions, developing a prototype, developing a mobile application, and implementation testing. We successfully introduced the FirstER (First for Emergency Room) platform on 23 September 2019. Additionally, validation in three tertiary hospitals has been carried out since the launch date. From 14 October to 29 November 2019, 1051 cases registered with the FirstER, and the total download count was 15,951 records. We developed and successfully implemented the mPHR service, which can be used as a health information exchange tool in emergency care, by integrating medical records from three different tertiary hospitals. By recognizing the significance and limitations of this service, it is necessary to study the development and implementation of mPHR services that are more suitable for emergency care. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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15 pages, 4246 KiB

Open AccessArticle

Analysis of Emergency Medical Vulnerability and Survival Rates Following Real-Time Traffic Information

by Jeongbae Jeon, Solhee Kim, Gu Hyun Kang and Kyo Suh

Appl. Sci. 2020, 10(18), 6492; https://doi.org/10.3390/app10186492 - 17 Sep 2020

Cited by 4 | Viewed by 3156

Abstract

Providing rapid access to emergency medical services (EMS) within the “golden time” for survival is important to improve the survival rate of emergency patients. This study analyzes the accessibility of EMS based on driving speed changes following real-time road traffic conditions by time to estimate vulnerable areas for EMS and survival rates of emergency patients. The key results of the network analysis based on real-time road speed and this evaluation of vulnerable areas by village level across South Korea reveal the different characteristics of urban and rural areas to access emergency medical facilities. In urban areas, road traffic congestion during rush hour delays the patients’ access time to EMS. In contrast, in rural areas, the long geographical distance to an emergency medical facility is a hurdle for receiving care from an EMS during the “golden time” because emergency medical facilities are mostly located in urban areas. The existing standard to assess vulnerable areas of EMS accessibility is based on the speed limit of roads, but the time may be underestimated because the speed limit alone does not reflect the real road conditions. The study results show that an effective way to increase the survival rate is receiving immediate first aid treatment, which means that the government should continuously train the public to perform cardiopulmonary resuscitation (CPR) as well as install automated external defibrillators (AEDs) in populated places, and train the public to use them. Reducing assess time to emergency medical centers in urban areas and providing additional manpower to help with first aid in rural areas are reasonable ways to improve the survival rate of emergency patients. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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Review

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13 pages, 926 KiB

Open AccessReview

Systematic Review of Nutritional Recommendation Systems

by Iñigo Orue-Saiz, Miguel Kazarez and Amaia Mendez-Zorrilla

Appl. Sci. 2021, 11(24), 12069; https://doi.org/10.3390/app112412069 - 17 Dec 2021

Cited by 7 | Viewed by 4485

Abstract

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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13 pages, 1500 KiB

Open AccessReview

The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance

by Hyunah Shin, Jaehun Cha, Chungchun Lee, Hyejin Song, Hyuntae Jeong, Jong-Yeup Kim and Suehyun Lee

Appl. Sci. 2021, 11(5), 2249; https://doi.org/10.3390/app11052249 - 4 Mar 2021

Cited by 3 | Viewed by 3229

Abstract

Pharmacovigilance, the scientific discipline pertaining to drug safety, has been studied extensively and is progressing continuously. In this field, medical informatics techniques and interpretation play important roles, and appropriate approaches are required. In this study, we investigated and analyzed the trends of pharmacovigilance systems, especially the data collection, detection, assessment, and monitoring processes. We used PubMed to collect papers on pharmacovigilance published over the past 10 years, and analyzed a total of 40 significant papers to determine the characteristics of the databases and data analysis methods used to identify drug safety indicators. Through systematic reviews, we identified the difficulty of standardizing data and terminology and establishing an adverse drug reactions (ADR) evaluation system in pharmacovigilance, and their corresponding implications. We found that appropriate methods and guidelines for active pharmacovigilance using medical big data are still required and should continue to be developed. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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20 pages, 2595 KiB

Open AccessReview

DynamiChain: Development of Medical Blockchain Ecosystem Based on Dynamic Consent System

by Tong Min Kim, Seo-Joon Lee, Dong-Jin Chang, Jawook Koo, Taenam Kim, Kun-Ho Yoon and In-Young Choi

Appl. Sci. 2021, 11(4), 1612; https://doi.org/10.3390/app11041612 - 10 Feb 2021

Cited by 20 | Viewed by 3502

Abstract

Although blockchain is acknowledged as one of the most important technologies to lead the fourth industrial revolution, major technical challenges regarding security breach and privacy issues remain. This issue is particularly sensitive in applied medical fields where personal health information is handled within the network. In addition, contemporary blockchain-converged solutions do not consider restricted medical data regulations that are still obstacles in many countries worldwide. This implies a crucial need for a system or solution that is suitable for the healthcare sector. Therefore, this article proposes the development of a dynamic consent medical blockchain system called DynamiChain, based on a ruleset management algorithm for handling health examination data. Moreover, medical blockchain-related studies were systematically reviewed to prove the novelty of DynamiChain. The proposed system was implemented in a scenario where the exercise management healthcare company provided health management services based on data obtained from the data provider’s hospital. The proposed research is envisioned to provide a widely compatible blockchain medical system that could be applied in future healthcare fields. Full article

(This article belongs to the Special Issue New Trends in Medical Informatics)

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