Special Issue "Artificial Intelligence for Health"

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

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Dr. Antonio Celesti
E-Mail Website1 Website2
Guest Editor
MIFT Department, University of Messina, Viale F. Stagno d'Alcontres, 31 98166 Messina, Italy
Interests: distributed systems; cloud computing; edge computing; Internet of Things (IoT); machine learning; assistive technology; eHealth
Special Issues and Collections in MDPI journals
Dr. Ivanoe De Falco
E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking (ICAR) of National Research Council (CNR), 80131 Naples, Italy
Interests: artificial intelligence; machine learning; soft computing; computational intelligence; parallel and distributed computing; explainable artificial intelligence; AI/ML applications to eHealth and mobile health; pattern recognition; signal processing; optimization; classification; regression; time series forecasting
Special Issues and Collections in MDPI journals
Dr. Antonino Galletta
E-Mail Website
Guest Editor
University of Messina, Italy
Interests: Big Data; Cloud Computing; Osmotic Computing; e-health.
Dr. Giovanna Sannino
E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking – National Research Council of Italy (ICAR-CNR)
Interests: Mobile Health; HealthCare Monitoring Systems; Pattern Recognition; Signal Processing; Artificial Intelligence

Special Issue Information

Dear Colleagues,

The 1st edition of the International Workshop on AI-driven Smart Healthcare (AIdSH) will be held on 7-11 December, 2020 in Taipei (Taiwan) within the 2020 edition of IEEE Global Communications Conference (GlobeCom).

For more information about the workshop, please use this link:

https://globecom2020.ieee-globecom.org/workshop/ws-11-workshop-ai-driven-smart-healthcare-aidsh

Health is one of the major research topics that have been attracting cross-disciplinary research groups. The deployment of new emerging ICT technologies for Health, especially based on Artificial Intelligence, Computational Intelligence, and Internet of Things (IoT), is attracting the interest of many researchers.

Selected papers which are presented at the workshop are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together in this Special Issue website. Accepted extended papers will be free of charge. There are no page limitations for this journal.

We are also inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in Artificial Intelligence for Health.

In light of the current pandemic, severely affecting the whole world, papers focusing on the application of the above ideas to any issue related to COVID-19 are highly welcome.

The main topics include, but are not limited to

  • Knowledge Management of Health Data;
  • Data Mining and Knowledge Discovery in Healthcare;
  • Machine and Deep Learning approaches for Health Data;
  • Explainable AI models for health, biology and medicine;
  • Decision Support Systems for Healthcare and Wellbeing;
  • AI for Precision Medicine;
  • Optimization for Healthcare problems;
  • Regression and Forecasting for medical and/or biomedical signals;
  • Healthcare Information Systems;
  • Wellness Information Systems;
  • Medical Signal and Image Processing and Techniques;
  • Medical Expert Systems;
  • Diagnoses and Therapy Support Systems;
  • Biomedical Applications;
  • Applications of AI in Healthcare and Wellbeing Systems;
  • Machine Learning-based Medical Systems;
  • Medical Data and Knowledge Bases;
  • Neural Networks in Medicine;
  • Ambient Intelligence and Pervasive Computing in Medicine and Healthcare;
  • AI in genomics;
  • AI for Healthcare Social Networks

Dr. Antonio Celesti
Dr. Ivanoe De Falco
Dr. Antonino Galletta
Dr. Giovanna Sannino
Guest Editors 

Published Papers (11 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy
Computers 2021, 10(4), 51; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10040051 - 13 Apr 2021
Viewed by 337
Abstract
Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of [...] Read more.
Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Graphical abstract

Open AccessArticle
Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques
Computers 2021, 10(3), 37; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10030037 - 19 Mar 2021
Viewed by 413
Abstract
The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state. This study is useful to identify 12 subtle emotions angry (annoying, angry, nervous), calm (calm, peaceful, [...] Read more.
The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state. This study is useful to identify 12 subtle emotions angry (annoying, angry, nervous), calm (calm, peaceful, relaxed), happy (excited, happy, pleased), sad (sleepy, bored, sad). A total 120 emotion signals were collected by using Emotive 14 channel EEG headset. Emotions are elicited by using three types of stimulus thoughts, audio and video. The system is trained by using captured database of emotion signals which include 30 signals of each emotion class. A total of 24 features were extracted by performing Chirplet transform. Band power is ranked as the prominent feature. The multimodel approach of classifier is used to classify emotions. Classification accuracy is tested for K-nearest neighbor (KNN), convolutional neural network (CNN), recurrent neural network (RNN) and deep neural network (DNN) classifiers. The system is tested to detect emotions of intellectually disable people. Meditation music therapy is used to stable mental state. It is found that it changed emotions of both intellectually disabled and normal participants from the annoying state to the relaxed state. A 75% positive transformation of mental state is obtained in the participants by using music therapy. This research study presents a novel approach for detailed analysis of brain EEG signals for emotion detection and stabilize mental state. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessArticle
A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance
by , and
Computers 2021, 10(3), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10030032 - 10 Mar 2021
Viewed by 359
Abstract
Monitoring the development of infectious diseases is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as early as possible by monitoring data sources which allow to capture the occurrences of a certain [...] Read more.
Monitoring the development of infectious diseases is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as early as possible by monitoring data sources which allow to capture the occurrences of a certain disease. Recent research mainly concentrates on the surveillance of specific, known diseases, putting the focus on the definition of the disease pattern under surveillance. Until now, only little effort has been devoted to what we call non-specific syndromic surveillance, i.e., the use of all available data for detecting any kind of infectious disease outbreaks. In this work, we give an overview of non-specific syndromic surveillance from the perspective of machine learning and propose a unified framework based on global and local modeling techniques. We also present a set of statistical modeling techniques which have not been used in a local modeling context before and can serve as benchmarks for the more elaborate machine learning approaches. In an experimental comparison of different approaches to non-specific syndromic surveillance we found that these simple statistical techniques already achieve competitive results and sometimes even outperform more elaborate approaches. In particular, applying common syndromic surveillance methods in a non-specific setting seems to be promising. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessArticle
A Sensitive Data Access Model in Support of Learning Health Systems
Computers 2021, 10(3), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10030025 - 26 Feb 2021
Viewed by 574
Abstract
Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning [...] Read more.
Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessArticle
A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture
Computers 2021, 10(1), 12; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010012 - 17 Jan 2021
Viewed by 824
Abstract
Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are [...] Read more.
Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessArticle
Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images
Computers 2021, 10(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010006 - 27 Dec 2020
Cited by 3 | Viewed by 969
Abstract
The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational [...] Read more.
The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images’ classification. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessArticle
Elderly Care Based on Hand Gestures Using Kinect Sensor
Computers 2021, 10(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010005 - 26 Dec 2020
Cited by 2 | Viewed by 915
Abstract
Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, [...] Read more.
Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessArticle
Trading-Off Machine Learning Algorithms towards Data-Driven Administrative-Socio-Economic Population Health Management
Computers 2021, 10(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010004 - 25 Dec 2020
Viewed by 715
Abstract
Together with population ageing, the number of people suffering from multimorbidity is increasing, up to more than half of the population by 2035. This part of the population is composed by the highest-risk patients, who are, at the same time, the major users [...] Read more.
Together with population ageing, the number of people suffering from multimorbidity is increasing, up to more than half of the population by 2035. This part of the population is composed by the highest-risk patients, who are, at the same time, the major users of the healthcare systems. The early identification of this sub-population can really help to improve people’s quality of life and reduce healthcare costs. In this paper, we describe a population health management tool based on state-of-the-art intelligent algorithms, starting from administrative and socio-economic data, for the early identification of high-risk patients. The study refers to the population of the Local Health Unit of Central Tuscany in 2015, which amounts to 1,670,129 residents. After a trade-off on machine learning models and on input data, Random Forest applied to 1-year of historical data achieves the best results, outperforming state-of-the-art models. The most important variables for this model, in terms of mean minimal depth, accuracy decrease and Gini decrease, result to be age and some group of drugs, such as high-ceiling diuretics. Thanks to the low inference time and reduced memory usage, the resulting model allows for real-time risk prediction updates whenever new data become available, giving General Practitioners the possibility to early adopt personalised medicine. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview
A Brief Review on the Sensor Measurement Solutions for the Ten-Meter Walk Test
Computers 2021, 10(4), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10040049 - 11 Apr 2021
Viewed by 320
Abstract
The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early [...] Read more.
The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early identification of various pathologies. An essential and reliable performance measure is the ten-meter walk test, which is employed to determine functional mobility, gait, and vestibular function. Sensor-based approaches can identify the various test phases and their segmented duration, among other parameters. The measurement parameter primarily used is related to the tests’ duration, and after identifying patterns, a variety of physical treatments can be recommended. This paper reviews multiple studies focusing on automated measurements of the ten-meter walk test with different sensors. Most of the analyzed studies measure similar parameters as traditional methods, such as velocity, duration, and other involuntary and dangerous patients’ movements after stroke. That provides an opportunity to measure different parameters that can be later fed into machine learning models for analyzing more complex patterns. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessReview
A Systematic Investigation of Models for Color Image Processing in Wound Size Estimation
Computers 2021, 10(4), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10040043 - 01 Apr 2021
Viewed by 293
Abstract
In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple non-invasive wound evaluation opportunities in both clinical and non-clinical settings. With current imaging [...] Read more.
In recent years, research in tracking and assessing wound severity using computerized image processing has increased. With the emergence of mobile devices, powerful functionalities and processing capabilities have provided multiple non-invasive wound evaluation opportunities in both clinical and non-clinical settings. With current imaging technologies, objective and reliable techniques provide qualitative information that can be further processed to provide quantitative information on the size, structure, and color characteristics of wounds. These efficient image analysis algorithms help determine the injury features and the progress of healing in a short time. This paper presents a systematic investigation of articles that specifically address the measurement of wounds’ sizes with image processing techniques, promoting the connection between computer science and health. Of the 208 studies identified by searching electronic databases, 20 were included in the review. From the perspective of image processing color models, the most dominant model was the hue, saturation, and value (HSV) color space. We proposed that a method for measuring the wound area must implement different stages, including conversion to grayscale for further implementation of the threshold and a segmentation method to measure the wound area as the number of pixels for further conversion to metric units. Regarding devices, mobile technology is shown to have reached the level of reliable accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Open AccessReview
Automated Machine Learning for Healthcare and Clinical Notes Analysis
Computers 2021, 10(2), 24; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020024 - 22 Feb 2021
Cited by 1 | Viewed by 836
Abstract
Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML [...] Read more.
Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence for Health)
Show Figures

Figure 1

Back to TopTop