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

Deadline for manuscript submissions: closed (15 April 2020) | Viewed by 33930

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Special Issue Editors

Prof. Chuan-Ming Liu

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Guest Editor

National Taipei University of Technology (Taipei Tech), Taiwan
Interests: big data management and processing; uncertain data management; data science; spatial data processing; data streams; ad-hoc and sensor networks; location-based services
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tian-Fu Lee

E-Mail Website
Guest Editor

Department of Medical Informatics, Tzu Chi University, Hualien 97004, Taiwan
Interests: cryptography; medical information security; wireless network; network security; sensor networks and HIPAA privacy/security regulations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of big data, the ways to manage, manipulate, analyze, and leverage data have been changed in most industries, including healthcare. The healthcare industry is thus changing at a dramatic rate. The results not only impact the care of individuals but also help medical practitioners and the delivery of care and services. The well-known characteristics of big data are the following Vs: Volume, Velocity, Variety, Veracity, Variability, and Value. New techniques for processing the data with such properties are becoming important and necessary. However, those properties also obviously pose new challenging problems. This Special Issue will host scientific discussions on the latest developments in the fields of Big Data Analytics in Healthcare. The main focus of this Special Issue will be on the proposal of methods and systems to process as well as extracting insights from medical health data. This issue is organized to invite authors to submit unpublished and original works. Potential topics include but are not limited to:

Topics:

(1) Healthcare and medical informatics;

(2) Artificial Intelligence in medicine;

(3) Biomedical data mining;

(4) Health information systems;

(5) Medical security and privacy;

(6) Hospital management;

(7) Big data management;

(8) Big data processing;

(9) Big data analytic tools;

(10) Big data for medical applications and health care.

Prof. Chuan-Ming Liu
Dr. Tian-Fu Lee
Guest Editors

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Published Papers (10 papers)

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Research

10 pages, 1315 KiB

Open AccessArticle

Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms

by Jesús F. Pérez-Gómez, Juana Canul-Reich, José Hernández-Torruco and Betania Hernández-Ocaña

Appl. Sci. 2020, 10(9), 3291; https://doi.org/10.3390/app10093291 - 9 May 2020

Cited by 2 | Viewed by 2264

Abstract

Requiring only a few relevant characteristics from patients when diagnosing bacterial vaginosis is highly useful for physicians as it makes it less time consuming to collect these data. This would result in having a dataset of patients that can be more accurately diagnosed using only a subset of informative or relevant features in contrast to using the entire set of features. As such, this is a feature selection (FS) problem. In this work, decision tree and Relief algorithms were used as feature selectors. Experiments were conducted on a real dataset for bacterial vaginosis with 396 instances and 252 features/attributes. The dataset was obtained from universities located in Baltimore and Atlanta. The FS algorithms utilized feature rankings, from which the top fifteen features formed a new dataset that was used as input for both support vector machine (SVM) and logistic regression (LR) algorithms for classification. For performance evaluation, averages of 30 runs of 10-fold cross-validation were reported, along with balanced accuracy, sensitivity, and specificity as performance measures. A performance comparison of the results was made between using the total number of features against using the top fifteen. These results found similar attributes from our rankings compared to those reported in the literature. This study is part of ongoing research that is investigating a range of feature selection and classification methods. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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14 pages, 741 KiB

Open AccessArticle

An Integrated Two-Stage Medical Pre-Checkup and Subsequent Validation Key Agreement Authentication Mechanism

by Tsung-Hung Lin and Ming-Te Chen

Appl. Sci. 2020, 10(5), 1888; https://doi.org/10.3390/app10051888 - 10 Mar 2020

Cited by 2 | Viewed by 2368

Abstract

In the global village era, several competitions require pre-checkups for the participants who are qualified to participate that must be passed before the competition, so the accuracy of the checkup data must be confirmed and must not be leaked or tampered with. This is a new challenge to the accuracy of medical checkups data in the information and communication era. How to protect the rights of participants and the non-repudiation of participants are the main issues of this study. We have designed a two-phase user identity embedding and authentication scheme for pre-checkups and subsequent validations. A participant’s private key is added to the physical examination data, and the identity of the examinations data is confirmed by the contestant before the competitions. Our work integrates lightweight Exclusive-OR (XOR) operations, fuzzy extractor biometric personal passwords, and a fixed-length hash operation accords with post-quantum operations to solve the problem of two-stage medical pre-checkup and subsequent validation key agreement authentication. The random oracle authentication mechanism proves the security of the protocols, and the security analysis proves that the protocols can resist the vulnerability attacks. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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12 pages, 1431 KiB

Open AccessArticle

Detection Performance Regarding Sleep Apnea-Hypopnea Episodes with Fuzzy Logic Fusion on Single-Channel Airflow Indexes

by Ming-Feng Wu, Wei-Chang Huang, Kai-Ming Chang, Po-Chun Lin, Chi-Hsuan Kuo, Cheng-Wei Hsu and Tsu-Wang Shen

Appl. Sci. 2020, 10(5), 1868; https://doi.org/10.3390/app10051868 - 9 Mar 2020

Cited by 5 | Viewed by 3180

Abstract

Obstructive sleep apnea-hypopnea syndrome (OSAHS) affects more than 936 million people worldwide and is the most common sleep-related breathing disorder; almost 80% of potential patients remain undiagnosed. To treat moderate to severe OSAHS as early as possible, the use of fewer sensing channels is recommended to screen for OSAHS and shorten waiting lists for the gold standard polysomnography (PSG). Hence, an effective out-of-clinic detection method may provide a solution to hospital overburden and associated health care costs. Applying single-channel signals to simultaneously detect apnea and hypopnea remains challenging. Among the various physiological signals used for sleep apnea-hypopnea detection, respiratory signals are relatively easy to apply. In this study, a fusion method using fuzzy logic and two single-channel respiratory indexes was proposed. A total of 12,391 apnea or hypopnea episodes were included. The proposed algorithm successfully fused standard deviation of airflow signals (SDA) and amplitude changes of peaks (ACP) indexes to detect apnea-hypopnea events, with overall sensitivity of 74%, specificity of 100%, and accuracy of 80% for mild to moderate OSAHS. For different apnea-hypopnea severity levels, the results indicated that the algorithm is superior to other methods; it also provides risk scores as percentages, which are especially accurate for mild hypopnea. The algorithm may provide rapid screening for early diagnosis and treatment. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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13 pages, 1764 KiB

Open AccessArticle

iPatient Privacy Copyright Cloud Management

by Yu-Jie (Jessica) Kuo and Jiann-Cherng Shieh

Appl. Sci. 2020, 10(5), 1863; https://doi.org/10.3390/app10051863 - 9 Mar 2020

Cited by 2 | Viewed by 2737

Abstract

The advent and rapid rise of network technology and cloud computing have led to new opportunities for ushering in a new era in telehealth. Thanks to the Internet of Things (IoT) and advances in 5G communication, telehealth is expanding and shows no signs of slowing down. It provides patients including elderly and disabled patients with convenient and easy access to healthcare services across space and time. However, the continuous real-time transmission of health information over networks also exposes private data to the risk of being intercepted by third parties. The privacy of the primary individual patient must be managed under the protection of the patient’s anonymous key while storing, transferring, sharing, and adding privacy rights. A question arises: How can we design a secure communication environment for remote access control to personal privacy matters? The patient’s electronic medical record is protected by the patient’s private key, and our scheme provides a real anonymous design for the patient with absolute autonomy over their privacy. Each update of the cloud medical records is patient-led and performed in a secure tunnel. As a result, this study reveals that the cloud-based iPatient privacy copyright management fully controlled by an individual patient is indeed safe and effective. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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18 pages, 2335 KiB

Open AccessArticle

An Integrated Approach to Biomedical Term Identification Systems

by Pilar López-Úbeda, Manuel Carlos Díaz-Galiano, Arturo Montejo-Ráez, María-Teresa Martín-Valdivia and L. Alfonso Ureña-López

Appl. Sci. 2020, 10(5), 1726; https://doi.org/10.3390/app10051726 - 3 Mar 2020

Cited by 11 | Viewed by 3221

Abstract

In this paper a novel architecture to build biomedical term identification systems is presented. The architecture combines several sources of information and knowledge bases to provide practical and exploration-enabled biomedical term identification systems. We have implemented a system to evidence the convenience of the different modules considered in the architecture. Our system includes medical term identification, retrieval of specialized literature and semantic concept browsing from medical ontologies. By applying several Natural Language Processing (NLP) technologies, we have developed a prototype that offers an easy interface for helping to understand biomedical specialized terminology present in Spanish medical texts. The result is a system that performs term identification of medical concepts over any textual document written in Spanish. It is possible to perform a sub-concept selection using the previously identified terms to accomplish a fine-tune retrieval process over resources like SciELO, Google Scholar and MedLine. Moreover, the system generates a conceptual graph which semantically relates all the terms found in the text. In order to evaluate our proposal on medical term identification, we present the results obtained by our system using the MANTRA corpus and compare its performance with the Freeling-Med tool. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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23 pages, 6092 KiB

Open AccessArticle

Big Data Analytics and Processing Platform in Czech Republic Healthcare

by Martin Štufi, Boris Bačić and Leonid Stoimenov

Appl. Sci. 2020, 10(5), 1705; https://doi.org/10.3390/app10051705 - 2 Mar 2020

Cited by 9 | Viewed by 7257

Abstract

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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13 pages, 1885 KiB

Open AccessArticle

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors

by Wen-Chien Ting, Horng-Rong Chang, Chi-Chang Chang and Chi-Jie Lu

Appl. Sci. 2020, 10(4), 1355; https://doi.org/10.3390/app10041355 - 17 Feb 2020

Cited by 20 | Viewed by 2962

Abstract

Colorectal cancer is ranked third and fourth in terms of mortality and cancer incidence in the world. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five machine learning classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. A total of 4287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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14 pages, 4684 KiB

Open AccessArticle

A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data

by Usman Akhtar, Anita Sant’Anna and Sungyoung Lee

Appl. Sci. 2019, 9(22), 4818; https://doi.org/10.3390/app9224818 - 11 Nov 2019

Cited by 3 | Viewed by 2498

Abstract

Vast amounts of data, especially in biomedical research, are being published as Linked Data. Being able to analyze these data sets is essential for creating new knowledge and better decision support solutions. Many of the current analytics solutions require continuous access to these data sets. However, accessing Linked Data at query time is prohibitive due to high latency in searching the content and the limited capacity of current tools to connect to these databases. To reduce this overhead cost, modern database systems maintain a cache of previously searched content. The challenge with Linked Data is that databases are constantly evolving and cached content quickly becomes outdated. To overcome this challenge, we propose a Change-Aware Maintenance Policy (CAMP) for updating cached content. We propose a Change Metric that quantifies the evolution of a Linked Dataset and determines when to update cached content. We evaluate our approach on two datasets and show that CAMP can reduce maintenance costs, improve maintenance quality and increase cache hit rates compared to standard approaches. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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12 pages, 1865 KiB

Open AccessArticle

Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks

by Mao-Sung Chen, I-Pin Chang and Tung-Kuan Liu

Appl. Sci. 2019, 9(20), 4320; https://doi.org/10.3390/app9204320 - 14 Oct 2019

Cited by 1 | Viewed by 2188

Abstract

Security is a critical issue for medical and health care systems. Password-based group-authenticated key agreement for wireless sensor networks (WSNs) allows a group of sensor nodes to negotiate a common session key by using password authentication and to establish a secure channel by this session key. Many group key agreement protocols use the public key infrastructure, modular exponential computations on an elliptic curve to provide high security, and thus increase sensor nodes’ overhead and require extra equipment for storing long-term secret keys. This work develops a novel group key agreement protocol using password authentication for WSNs, which is based on extended chaotic maps and does not require time-consuming modular exponential computations or scalar multiplications on an elliptic curve. Additionally, the proposed protocol is suitable for multiple independent groups and ensures that the real identities of group members cannot be revealed. The proposed protocol is not only more secure than related group key agreement protocols but also more efficient. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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13 pages, 2270 KiB

Open AccessArticle

Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

by Ji-Soo Kang, Dong-Hoon Shin, Ji-Won Baek and Kyungyong Chung

Appl. Sci. 2019, 9(20), 4284; https://doi.org/10.3390/app9204284 - 12 Oct 2019

Cited by 14 | Viewed by 3898

Abstract

Korean people are exposed to stress due to the constant competitive structure caused by rapid industrialization. As a result, there is a need for ways that can effectively manage stress and help improve quality of life. Therefore, this study proposes an activity recommendation model using rank correlation for chronic stress management. Using Spearman’s rank correlation coefficient, the proposed model finds the correlations between users’ Positive Activity for Stress Management (PASM), Negative Activity for Stress Management (NASM), and Perceived Stress Scale (PSS). Spearman’s rank correlation coefficient improves the accuracy of recommendations by putting a basic rank value in a missing value to solve the sparsity problem and cold-start problem. For the performance evaluation of the proposed model, F-measure is applied using the average precision and recall after five times of recommendations for 20 users. As a result, the proposed method has better performance than other models, since it recommends activities with the use of the correlation between PASM and NASM. The proposed activity recommendation model for stress management makes it possible to manage user’s stress effectively by lowering the user’s PSS using correlation. Full article

(This article belongs to the Special Issue Big Data Analytics in Healthcare)

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