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Selected Papers From the pHealth 2024 Conference, Rende, Italy, 27–29...
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Selected Papers From the pHealth 2024 Conference, Rende, Italy, 27–29 May 2024

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1423

Special Issue Editors

Dr. Mauro Giacomini

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Guest Editor
Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145 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
Prof. Dr. Bernd Blobel

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Guest Editor
Faculty of Medicine, University of Regensburg, 93053 Regensburg, Germany
Interests: systems medicine; interoperability; data security; HL7; eHealth; medical informatics; electronic health records; health informatics; healthcare IT; oncology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pierangelo Veltri

E-Mail Website
Guest Editor
Department of Computer Science, Modelling, Electronics and Systems, University of Calabria, 87036 Rende, Italy
Interests: proteomics; health data modelling and management; bioinformatics; bioimaging; artifical neural networks; epidemiological data management;

Special Issue Information

Dear Colleagues,

pHealth 2024 is the 20th event in the pHealth conferences series, starting in 2003 as a dissemination activity in the framework of a European Project on Wearable Micro and Nano Technologies for Personalized Health with personal health management systems. Since then, pHealth conferences have evolved to be truly interdisciplinary, global scientific events covering the medical, technological, political, administrative, and social domains, and even philosophical or linguistic challenges of personalized health in transforming health systems. In the last 2–3 years, the focus has turned towards the 5P medicine paradigm of personalized, preventive, predictive, and participative precision medicine, knowledge representation and management, and the deployment of artificial intelligence and machine learning. This pHealth 2024 Special Issue presents the best papers selected from the conference, which took place on 27–29 May 2024 in Rende, Italy. It includes the keynote to the conference, one invited paper, and ten regular papers. Furthermore, it contains two papers awarded the CaseMix Young Scientists Best Paper Award, as well as one panel addressing the perspectives from the Italian Society for Biomedical Informatics (SIBIM) on education in health informatics. Thus, standardization in the field, security, privacy and trustworthiness, and learning systems as well as AI, but also practical solutions, are demonstrated.


Publisher’s Notice

As stated above, the central purpose of this Special Issue is to present research from "Selected Papers from the pHealth 2024 Conference, Rende, Italy, 27–29 May 2024. Given this purpose, the Guest Editors’ contribution to this Special Issue may be greater than standard Special Issues published by MDPI. Further details on MDPI's Special Issue guidelines can be found here: https://www.mdpi.com/special_issues_guidelines. The Editorial Office and Editor-in-Chief of JPM have approved this, and MDPI’s standard manuscript editorial processing procedure (https://www.mdpi.com/editorial_process) will be applied to all submissions. As per our standard procedure, Guest Editors are excluded from participating in the editorial process for their submission and/or for submissions from persons with whom a potential conflict of interest may exist. More details on MDPI’s conflict of interest policy for reviewers and editors can be found at the following link: https://www.mdpi.com/ethics#_bookmark22.

Dr. Mauro Giacomini
Prof. Dr. Bernd Blobel
Prof. Dr. Pierangelo Veltri
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. Journal of Personalized Medicine 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 2600 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

  • 5P medicine
  • transformed health ecosystems
  • systems integration and interoperability
  • knowledge representation and management

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Published Papers (3 papers)

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Research

19 pages, 401 KiB  
Article
A Comprehensive Dataset for Activity of Daily Living (ADL) Research Compiled by Unifying and Processing Multiple Data Sources
by Jaime Pabón, Daniel Gómez, Jesús D. Cerón, Ricardo Salazar-Cabrera, Diego M. López and Bernd Blobel
J. Pers. Med. 2025, 15(5), 210; https://doi.org/10.3390/jpm15050210 (registering DOI) - 21 May 2025
Abstract
Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. [...] Read more.
Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. Methods: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. Results: The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. Conclusions: In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models. Full article
(This article belongs to the Special Issue Selected Papers From the pHealth 2024 Conference, Rende, Italy, 27–29 May 2024)
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24 pages, 1212 KiB  
Article
Comparative Evaluation of Automatic Detection and Classification of Daily Living Activities Using Batch Learning and Stream Learning Algorithms
by Paula Sofía Muñoz, Ana Sofía Orozco, Jaime Pabón, Daniel Gómez, Ricardo Salazar-Cabrera, Jesús D. Cerón, Diego M. López and Bernd Blobel
J. Pers. Med. 2025, 15(5), 208; https://doi.org/10.3390/jpm15050208 - 20 May 2025
Abstract
Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating [...] Read more.
Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring. Full article
(This article belongs to the Special Issue Selected Papers From the pHealth 2024 Conference, Rende, Italy, 27–29 May 2024)
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21 pages, 5177 KiB  
Article
The Representational Challenge of Integration and Interoperability in Transformed Health Ecosystems
by Bernd Blobel, Frank Oemig, Pekka Ruotsalainen, Mathias Brochhausen, Kevin W. Sexton and Mauro Giacomini
J. Pers. Med. 2025, 15(1), 4; https://doi.org/10.3390/jpm15010004 - 25 Dec 2024
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Abstract
Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This [...] Read more.
Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. Methods: To enable communication and cooperation between actors from different domains using different methodologies, languages, and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystem and all its components in terms of structure, function and relationships in the necessary detail, ranging from elementary particles up to the universe. In this way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct, and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business viewpoint of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Results: The model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture. The formal representation of any ecosystem and its development process including examples of practical deployment of the approach, are presented in detail. This includes correct systems and standards integration and interoperability solutions. A special issue newly addressed in the paper is the correct and consistent formal representation Conclusions: of all components in the development process, enabling interoperability between and integration of any existing representational artifacts such as models, work products, as well as used terminologies and ontologies. The provided solution is meanwhile mandatory at ISOTC215, CEN/TC251 and many other standards developing organization in health informatics for all projects covering more than just one domain. Full article
(This article belongs to the Special Issue Selected Papers From the pHealth 2024 Conference, Rende, Italy, 27–29 May 2024)
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