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Computers
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Mobile4Medicine 2022: Mobile Systems and Pervasive Computing for Personalized Medicine
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Mobile4Medicine 2022: Mobile Systems and Pervasive Computing for Personalized Medicine

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (11 November 2022) | Viewed by 6712

Special Issue Editor

Dr. Ivan Miguel Serrano Pires

E-Mail Website
Guest Editor
1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal
2. Polytechnic Institute of Viseu, Viseu, Portugal
Interests: ambient assisted living technologies; health; sensor-based systems; machine learning; mobile innovative technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medicine is under a constant evolution, but different people require different treatments. It is considered in the subject of personalized medicine, where treatments are adapted to the patients. Technology offers wide possibilities of patient empowerment, including sensors and mobile devices. It allows the acquisition of different data and the remote application of different treatments. These developments are mainly included in information and communication technologies (ICT) for wellbeing and healthcare.

Telehealth and telemedicine refer to the use of telecommunications and virtual technology to deliver healthcare. Technological development has transformed healthcare. Systems evolution, including data analytics, artificial intelligence, the Internet of Things, increased connection and the use of mobile devices, has allowed this transformation. Telemedicine developed due to the increasing availability of systems and access but also due to the growing demand of solutions during the COVID-19 pandemic. Recent trends have changed healthcare monitoring and recovery from healthcare institutions to patient-centered care delivered at home. Additionally, patient empowerment is a focal point in healthcare evolution, to allow patients to manage their health and adapt to chronic diseases. Technological advancements play a central role in the promotion of patient empowerment by providing access and remote support that might help to overcome challenges of geographic location and promote equal access to healthcare.

In the case of medical rehabilitation and assistive technology, research in applications of ICT have contributed greatly to the enhancement of quality of life and the full integration of all citizens into society. The use of cloud computing and network connections exploits the exchange of information and improves these systems in different fields, including healthcare and traffic management. These developments should be closely performed with the participation of healthcare professionals. There are different solutions currently under development related to this field and, as it contributes to improving the quality of life, it can help in the development of technologies for social care. It is a wide, multidisciplinary subject regarding wellbeing. Authors from all fields working in these subjects are encouraged to submit a paper presenting their recent work, or a scientific discussion on the topic.

Authors are invited to submit original, previously unpublished papers based on topics including, but not limited to, the following:

  • Healthcare information system interoperability, security and efficiency;
  • Ambient intelligence for wellbeing and e-health applications, supported by RFID technology and wireless sensor networks;
  • Mobile applications and ubiquitous devices in healthcare and lifestyle training;
  • Robotic systems and devices for healthcare and medicine;
  • Technologies to promote a healthy and secure society;
  • Big data analytics for e-health;
  • Machine learning for healthcare;
  • Intelligent decision support and data systems in healthcare, medicine and society;
  • Innovation in people-supporting activities (e.g., healthcare, schooling and services);
  • Embedded systems for healthcare;
  • Biosignal acquisition, analysis and processing;
  • Telemedicine;
  • Physiological computing in mobile devices;
  • Augmented reality in healthcare using wearable devices;
  • Cloud computing for healthcare;
  • Mobile application concepts and technologies for different mobile platforms.

Dr. Ivan Miguel Serrano Pires
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. Computers 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 1800 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.

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Published Papers (2 papers)

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Research

17 pages, 924 KiB  
Article
PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts Using Transfer Learning
by Nasi Jofche, Kostadin Mishev, Riste Stojanov, Milos Jovanovik, Eftim Zdravevski and Dimitar Trajanov
Computers 2023, 12(1), 17; https://doi.org/10.3390/computers12010017 - 9 Jan 2023
Cited by 4 | Viewed by 3303
Abstract
Even though named entity recognition (NER) has seen tremendous development in recent years, some domain-specific use-cases still require tagging of unique entities, which is not well handled by pre-trained models. Solutions based on enhancing pre-trained models or creating new ones are efficient, but [...] Read more.
Even though named entity recognition (NER) has seen tremendous development in recent years, some domain-specific use-cases still require tagging of unique entities, which is not well handled by pre-trained models. Solutions based on enhancing pre-trained models or creating new ones are efficient, but creating reliable labeled training for them to learn on is still challenging. In this paper, we introduce PharmKE, a text analysis platform tailored to the pharmaceutical industry that uses deep learning at several stages to perform an in-depth semantic analysis of relevant publications. The proposed methodology is used to produce reliably labeled datasets leveraging cutting-edge transfer learning, which are later used to train models for specific entity labeling tasks. By building models for the well-known text-processing libraries spaCy and AllenNLP, this technique is used to find Pharmaceutical Organizations and Drugs in texts from the pharmaceutical domain. The PharmKE platform also incorporates the NER findings to resolve co-references of entities and examine the semantic linkages in each phrase, creating a foundation for further text analysis tasks, such as fact extraction and question answering. Additionally, the knowledge graph created by DBpedia Spotlight for a specific pharmaceutical text is expanded using the identified entities. The obtained results with the proposed methodology result in about a 96% F1-score on the NER tasks, which is up to 2% better than those of the fine-tuned BERT and BioBERT models developed using the same dataset. The ultimate benefits of the platform are that pharmaceutical domain specialists may more easily identify the knowledge extracted from the input texts thanks to the platform’s visualization of the model findings. Likewise, the proposed techniques can be integrated into mobile and pervasive systems to give patients more relevant and comprehensive information from scanned medication guides. Similarly, it can provide preliminary insights to patients and even medical personnel on whether a drug from a different vendor is compatible with the patient’s prescription medication. Full article
(This article belongs to the Special Issue Mobile4Medicine 2022: Mobile Systems and Pervasive Computing for Personalized Medicine)
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20 pages, 7779 KiB  
Article
Identification of Heart Arrhythmias by Utilizing a Deep Learning Approach of the ECG Signals on Edge Devices
by Panagiotis Seitanidis, John Gialelis, Georgia Papaconstantinou and Alexandros Moschovas
Computers 2022, 11(12), 176; https://doi.org/10.3390/computers11120176 - 4 Dec 2022
Cited by 3 | Viewed by 2729
Abstract
Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, [...] Read more.
Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3%, mean sensitivity of 95.27%, mean specificity of 98.82%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA® Jetson Nano™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios. Full article
(This article belongs to the Special Issue Mobile4Medicine 2022: Mobile Systems and Pervasive Computing for Personalized Medicine)
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