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Artificial Intelligence for Daily Health and Motion Management
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Artificial Intelligence for Daily Health and Motion Management

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 (1 August 2020) | Viewed by 11610

Special Issue Editors

Dr. Giuseppe De Pietro

E-Mail Website
Guest Editor
National Research Council of Italy (CNR)—Institute for High Performance Computing & Networking (ICAR), Via P Castellino 111, 80131 Naples, Italy
Interests: decision support systems; pervasive computing; e-health
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Esposito

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), 80131 Naples, Italy
Interests: artificial Intelligence; natural language processing; knowledge engineering; conversational systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the rapid advance of Artificial Intelligence in many medical fields, on the one hand, and of novel Internet of Things technologies to track health statistics, such as humans’ daily activity and vital signs, on the other hand, new opportunities are being created for health and medicine.

Despite the recent remarkable success of AI and, in particular, of deep learning, in different clinical scenarios, significant challenges still remain with reference to the application of AI and IoT technologies to daily assist patients at their homes and early and consistently treat many health issues before reaching critical stages. Indeed, currently, AI solutions for health monitoring and care at patients’ homes are still at an early stage, with a very few real-world implementations. Thus, the real feasibility of such AI applications in everyday activities and settings is unknown and requires further investigations and validation.

This Special Issue is intended to provide an overview of the research being carried out to address the challenging aspects of AI application for daily health management. A particular focus will be given on emerging approaches and real applications of AI to support programmes for daily and personalized health monitoring, assistance and rehabilitation at home. To this end, the Special Issue aims to gather researchers with a broad expertise in various fields—computer vision, natural language and signal processing, statistics logics and neural networks, human–computer interaction and robotics—to discuss their cutting-edge work as well as perspectives on future directions in this exciting field. Original contributions are sought, covering the whole range of theoretical and practical aspects, technologies, and systems in this research area.

The topics of interest for this Special Issue include but are not limited to:

  • Big data and predictive analytics;
  • Machine/deep learning, knowledge discovery, and data mining;
  • Knowledge representation and human-like reasoning;
  • Computer vision and pattern recognition;
  • Signal processing;
  • Reinforcement learning;
  • Human–computer interaction;
  • Natural language processing and conversational systems/interfaces;
  • Cognitive and social robotics;
  • Sentiment analysis, emotion detection, and opinion mining;
  • Trustworthy and explainable artificial intelligence;
  • AI solutions for ambient assisted living, telemedicine and e-health.

Eng. Dr. Giuseppe De Pietro
Dr. Massimo Esposito
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.

Keywords

  • Artificial intelligence
  • Decision support systems
  • e-health
  • Big data
  • Machine/deep learning

Published Papers (3 papers)

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Research

12 pages, 1582 KiB  
Article
SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence
by Marco Pota, Andrea Pota, Maria Luisa Sirico and Massimo Esposito
Int. J. Environ. Res. Public Health 2020, 17(24), 9290; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17249290 - 11 Dec 2020
Cited by 3 | Viewed by 2339
Abstract
COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of [...] Read more.
COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured). Full article
(This article belongs to the Special Issue Artificial Intelligence for Daily Health and Motion Management)
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19 pages, 3050 KiB  
Article
Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia
by Haneen Reda Banjar, Heba Alkhatabi, Nofe Alganmi and Ghaidaa Ibraheem Almouhana
Int. J. Environ. Res. Public Health 2020, 17(21), 8066; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17218066 - 02 Nov 2020
Cited by 10 | Viewed by 6411
Abstract
The increasing number of COVID-19 patients has increased health care professionals’ workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff [...] Read more.
The increasing number of COVID-19 patients has increased health care professionals’ workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff to handle the increased number of patients. Although Saudi Arabia has recently improved the quality of its health services, there is still no suitable intelligent system that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan remotely, which would allow for the effective follow-up of patients of COVID-19. The proposed system includes five sub-systems: an information management system, a knowledge-based expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system. This study shows that, to control epidemics, there is a method to overcome the shortage of specialists in the management of infections in Saudi Arabia, both today and in the future. The availability of computerized clinical guidance and an up-to-date knowledge base play a role in Saudi health organizations, which may not have to constantly train their physician staff and may no longer have to rely on international experts, since the expert system can offer clinicians all the information necessary to treat their patients. Full article
(This article belongs to the Special Issue Artificial Intelligence for Daily Health and Motion Management)
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11 pages, 596 KiB  
Article
A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension
by Bin Wang, Xuejie Zhang, Xiaobing Zhou and Junyi Li
Int. J. Environ. Res. Public Health 2020, 17(4), 1323; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17041323 - 19 Feb 2020
Cited by 11 | Viewed by 2327
Abstract
The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehension. In this paper, we study the cloze-style machine [...] Read more.
The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehension. In this paper, we study the cloze-style machine reading comprehension in the clinical medical field and propose a Gated Dilated Convolution with Attention (GDCA) model, which consists of a gated dilated convolution module and an attention mechanism. Our model has high parallelism and is capable of capturing long-distance dependencies. On the CliCR data set, our model surpasses the present best model on several metrics and obtains state-of-the-art result, and the training speed is 8 times faster than that of the best model. Full article
(This article belongs to the Special Issue Artificial Intelligence for Daily Health and Motion Management)
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