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Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 22109

Special Issue Editors

Prof. Dr. Bernd Blobel

E-Mail Website
Guest Editor
Faculty of Medicine, University of Regensburg, Regensburg, Germany
Interests: interoperability; data security; HL7; eHealth; medical informatics; electronic health records; health informatics; healthcare IT; oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Mauro Giacomini

E-Mail Website
Guest Editor
Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
Interests: antibiotics; environment; infection; oncology; biodiversity; analysis; neural networks; classification; information technology; artificial neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You have been invited to submit a journal version of your paper presented at the pHealth 2019 Conference in Genoa, 10-12 June 2019, selected by the International Jury for publication in the pHealth 2019 Special Issue of IJERPH (Impact Factor 2.468). This Special Issue will be guest edited by Prof. Dr. Bernd Blobel and Prof. Dr. Mauro Giacomini. Please submit your manuscripts before 30 September 2019. See the Special Issue website for further details and submission instructions. Participants of this conference will receive a 20% discount on the Article Processing Charges.

Papers submitted to this Special Issue of IJERPH will undergo the standard peer-review procedure. Published papers will be indexed by the SCIE (Web of Science) and PubMed.

Prof. Dr. Bernd Blobel
Prof. Dr. Mauro Giacomini
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.

Published Papers (6 papers)

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Research

16 pages, 1504 KiB  
Article
Personalized Tracking of Physical Activity in Children Using a Wearable Heart Rate Monitor
by Santiago A. Pérez, Ana M. Díaz and Diego M. López
Int. J. Environ. Res. Public Health 2020, 17(16), 5895; https://doi.org/10.3390/ijerph17165895 - 14 Aug 2020
Cited by 4 | Viewed by 2500
Abstract
Serious games are video games that are intended to support learning while entertaining. They are considered valuable tools to improve user-specific skills or facilitate educational or therapeutic processes, especially in children. One of the disadvantages of computer games, in general, is their promotion [...] Read more.
Serious games are video games that are intended to support learning while entertaining. They are considered valuable tools to improve user-specific skills or facilitate educational or therapeutic processes, especially in children. One of the disadvantages of computer games, in general, is their promotion of sedentary habits, considered as a significant risk factor for developing diseases such as obesity and hypertension. Exergames are serious games created to overcome the disadvantages of traditional computer games by promoting physical activity while playing. This study describes the development and evaluation of an adaptive component to monitor physical activity in children while using an exergame. The system is based on wearable technology to measure heart rate and perform real-time customizations in the exergame. To evaluate the adaptive component, an experiment was conducted with 30 children between 5 and 7 years of age, where the adaptive system was contrasted with a conventional interactive system (an exergame without adaptive component). It was demonstrated that the computer game, using the adaptive component, was able to change in real-time some of its functionalities based on the user characteristics. Increased levels of heart rate and caloric expenditure were significant in some of the game scenarios using the adaptive component. Although a formal user experience evaluation was not performed, excellent game playability and adherence by users were observed. Full article
(This article belongs to the Special Issue Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019)
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15 pages, 1421 KiB  
Article
Health Information Systems in the Digital Health Ecosystem—Problems and Solutions for Ethics, Trust and Privacy
by Pekka Ruotsalainen and Bernd Blobel
Int. J. Environ. Res. Public Health 2020, 17(9), 3006; https://doi.org/10.3390/ijerph17093006 - 26 Apr 2020
Cited by 28 | Viewed by 5624
Abstract
Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject’s perspective, there is frequently no guarantee and therefore [...] Read more.
Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject’s perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors’ objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user’s privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems. Full article
(This article belongs to the Special Issue Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019)
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15 pages, 721 KiB  
Article
Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification
by César A. Millán, Nathalia A. Girón and Diego M. Lopez
Int. J. Environ. Res. Public Health 2020, 17(2), 498; https://doi.org/10.3390/ijerph17020498 - 13 Jan 2020
Cited by 15 | Viewed by 2973
Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world’s population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a [...] Read more.
Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world’s population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time–frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%). Full article
(This article belongs to the Special Issue Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019)
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16 pages, 2307 KiB  
Article
Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance
by Roberta Gazzarata, Maria Eugenia Monteverde, Carmelina Ruggiero, Norbert Maggi, Dalia Palmieri, Giustino Parruti and Mauro Giacomini
Int. J. Environ. Res. Public Health 2020, 17(2), 465; https://doi.org/10.3390/ijerph17020465 - 10 Jan 2020
Cited by 7 | Viewed by 3032
Abstract
Prevention and surveillance of healthcare associated infections caused by multidrug resistant organisms (MDROs) has been given increasing attention in recent years and is nowadays a major priority for health care systems. The creation of automated regional, national and international surveillance networks plays a [...] Read more.
Prevention and surveillance of healthcare associated infections caused by multidrug resistant organisms (MDROs) has been given increasing attention in recent years and is nowadays a major priority for health care systems. The creation of automated regional, national and international surveillance networks plays a key role in this respect. A surveillance system has been designed for the Abruzzo region in Italy, focusing on the monitoring of the MDROs prevalence in patients, on the appropriateness of antibiotic prescription in hospitalized patients and on foreseeable interactions with other networks at national and international level. The system has been designed according to the Service Oriented Architecture (SOA) principles, and Healthcare Service Specification (HSSP) standards and Clinical Document Architecture Release 2 (CDAR2) have been adopted. A description is given with special reference to implementation state, specific design and implementation choices and next foreseeable steps. The first release will be delivered at the Complex Operating Unit of Infectious Diseases of the Local Health Authority of Pescara (Italy). Full article
(This article belongs to the Special Issue Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019)
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18 pages, 14786 KiB  
Article
Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
by Ilia Semenov, Roman Osenev, Sergey Gerasimov, Georgy Kopanitsa, Dmitry Denisov and Yuriy Andreychuk
Int. J. Environ. Res. Public Health 2020, 17(1), 73; https://doi.org/10.3390/ijerph17010073 - 20 Dec 2019
Cited by 20 | Viewed by 5096
Abstract
This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely [...] Read more.
This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models. Full article
(This article belongs to the Special Issue Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019)
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8 pages, 733 KiB  
Article
Comparison of Word Embeddings for Extraction from Medical Records
by Aleksei Dudchenko and Georgy Kopanitsa
Int. J. Environ. Res. Public Health 2019, 16(22), 4360; https://doi.org/10.3390/ijerph16224360 - 08 Nov 2019
Cited by 9 | Viewed by 2410
Abstract
This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from [...] Read more.
This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763). Full article
(This article belongs to the Special Issue Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019)
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