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Machine Learning and Artificial Intelligence for Health Applications on Social...
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Machine Learning and Artificial Intelligence for Health Applications on Social Networks

A topical collection in Big Data and Cognitive Computing (ISSN 2504-2289).

Viewed by 51156

Editor

Dr. Carmela Comito

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), Rome, Italy
Interests: big data analysis and mining; mobility mining; social network data analysis and mining; health informatics
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

The fast expansion of social media and social networks in the last few years is making available an enormous and continuous stream of user-generated contents containing invaluable information that can be used to understand, in near real time, human life dynamics worldwide. These massive quantities of data could support a wide range of medical and healthcare applications, including, among others, clinical trials and decision support, disease surveillance, personalized medicines, and population health management. Besides, social media combines textual, temporal, geographical, and network data, opening up unique opportunities to study the interplay between human mobility, social structure and disease transmission.

In this context, Artificial intelligence (AI) and machine learning techniques can play a crucial role to deal with such amount of heterogeneous, multi-scale and multi-modal data. Some examples of techniques that are gaining attention in this domain include deep learning, domain adaptation, semi-supervised approach, time series analysis, and active learning. Even though the use of machine learning and the development of ad-hoc techniques are gaining increasing popularity in the health domain, we can witness that a significant lack of interaction between domain experts and machine learning researchers still exists.

AI is changing the landscape of healthcare and modern personalized precision medicine. With the increasing availability of healthcare data and rapid progress of machine learning algorithms and analysis techniques, AI is gradually enabling doctors with tools for better diagnosis, disease surveillance, facilitating early detection, uncovering novel treatments, and creating an era of truly personalized medicine. In particular, novel approaches to AI and big data analytics and mining methods on social media data and social networks may provide invaluable means for health monitoring, surveillance, disease spreading, and outbreak prediction.

Artificial intelligence in healthcare is going play a significant role in solving the issues like drug-interaction, false alarms, over-diagnosis, and over-treatment. Moreover, AI with new technologies of IoT and Blockchain has tremendous scope for better medical treatment with data security.

The main areas of AI applications in healthcare are: providing personalized precision medicine, analysis and interpretation of radiology images, automated diagnosis, prescription preparation, clinical workflow monitoring, patient monitoring and care, discovery of new drugs, predicting the impact of gene edits, treatment protocol development, and early diagnoses of diseases.

The Special Issue solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on social networks analysis and mining on topics in the realm of healthcare and health informatics along with applications to real life situations.

Dr. Carmela Comito
Guest Editor

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Keywords

  • Personal health virtual assistant
  • Early disease diagnosis and treatment prediction
  • Clinical decision support in disease diagnosis and treatment
  • Analysis and interpretation of radiology images
  • Application of deep learning methods to health data
  • Spatio-temporal prediction of pandemics
  • Modeling the health status and well-being of individuals
  • Real-time syndromic surveillance and early detection of emerging disease
  • Drug adversial reaction
  • Drug abuse and alcoholism incidence monitoring
  • Medical imaging analysis and diagnosis assistance
  • mHealth, eHealth, and Wearable Health
  • Blockchain for healthcare
  • Social media data analyses and mining for public health
  • Novel methods and frameworks for mining and integrating big health data
  • Semantics and interoperability for healthcare data
  • Clinical natural language processing and text mining
  • Predictive modelling for diagnosis and treatment
  • Data privacy and security for healthcare data
  • Medical fraud detection
  • Data analytics for pervasive computing for medical care

Published Papers (9 papers)

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2023

Jump to: 2022

12 pages, 1508 KiB  
Opinion
Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists
by Anna M. Girardi, Elizabeth A. Cardell and Stephen P. Bird
Big Data Cogn. Comput. 2023, 7(4), 178; https://doi.org/10.3390/bdcc7040178 - 28 Nov 2023
Cited by 1 | Viewed by 1847
Abstract
Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), especially deep learning (DL) models, has enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently, it has important implications for swallow diagnostics and intervention planning. However, the [...] Read more.
Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), especially deep learning (DL) models, has enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently, it has important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallow studies (VFSS) is still emerging. This review showcases the recent literature on the use of AI to interpret VFSS and highlights clinical implications for speech–language pathologists (SLPs). With a surge in AI research, there have been advances in dysphagia assessments. Several studies have demonstrated the successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs), which involve training a multi-layered model to recognize specific image or video components, have been used to detect pertinent aspects of the swallowing process with high levels of precision. DL algorithms have the potential to streamline VFSS interpretation, improve efficiency and accuracy, and enable the precise interpretation of an instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing the precision, speed, and depth of VFSS interpretation, SLPs can obtain a more comprehensive understanding of swallow physiology and deliver a targeted and timely intervention that is tailored towards the individual. This has practical applications for both clinical practice and dysphagia research. As this research area grows and AI technologies progress, the application of DL in the field of VFSS interpretation is clinically beneficial and has the potential to transform dysphagia assessment and management. With broader validation and inter-disciplinary collaborations, AI-augmented VFSS interpretation will likely transform swallow evaluations and ultimately improve outcomes for individuals with dysphagia. However, despite AI’s potential to streamline imaging interpretation, practitioners still need to consider the challenges and limitations of AI implementation, including the need for large training datasets, interpretability and adaptability issues, and the potential for bias. Full article
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18 pages, 2033 KiB  
Article
Machine Learning-Based Identifications of COVID-19 Fake News Using Biomedical Information Extraction
by Faizi Fifita, Jordan Smith, Melissa B. Hanzsek-Brill, Xiaoyin Li and Mengshi Zhou
Big Data Cogn. Comput. 2023, 7(1), 46; https://doi.org/10.3390/bdcc7010046 - 07 Mar 2023
Cited by 3 | Viewed by 3702
Abstract
The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 [...] Read more.
The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic. Full article
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18 pages, 5142 KiB  
Article
Analyzing the Performance of Transformers for the Prediction of the Blood Glucose Level Considering Imputation and Smoothing
by Edgar Acuna, Roxana Aparicio and Velcy Palomino
Big Data Cogn. Comput. 2023, 7(1), 41; https://doi.org/10.3390/bdcc7010041 - 23 Feb 2023
Cited by 1 | Viewed by 2110
Abstract
In this paper we investigate the effect of two preprocessing techniques, data imputation and smoothing, in the prediction of blood glucose level in type 1 diabetes patients, using a novel deep learning model called Transformer. We train three models: XGBoost, a one-dimensional convolutional [...] Read more.
In this paper we investigate the effect of two preprocessing techniques, data imputation and smoothing, in the prediction of blood glucose level in type 1 diabetes patients, using a novel deep learning model called Transformer. We train three models: XGBoost, a one-dimensional convolutional neural network (1D-CNN), and the Transformer model to predict future blood glucose levels for a 30-min horizon using a 60-min time series history in the OhioT1DM dataset. We also compare four methods of handling missing time series data during the model training: hourly mean, linear interpolation, cubic interpolation, and spline interpolation; and two smoothing techniques: Kalman smoothing and smoothing splines. Our experiments show that the Transformer performs better than XGBoost and 1D-CNN when only continuous glucose monitoring (CGM) is used as a predictor, and that it is very competitive against XGBoost when CGM and carbohydrate intake from the meal are used to predict blood glucose level. Overall, our results are more accurate than those appearing in the literature. Full article
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2022

Jump to: 2023

41 pages, 6572 KiB  
Review
A Systematic Literature Review on Diabetic Retinopathy Using an Artificial Intelligence Approach
by Pooja Bidwai, Shilpa Gite, Kishore Pahuja and Ketan Kotecha
Big Data Cogn. Comput. 2022, 6(4), 152; https://doi.org/10.3390/bdcc6040152 - 08 Dec 2022
Cited by 9 | Viewed by 8273
Abstract
Diabetic retinopathy occurs due to long-term diabetes with changing blood glucose levels and has become the most common cause of vision loss worldwide. It has become a severe problem among the working-age group that needs to be solved early to avoid vision loss [...] Read more.
Diabetic retinopathy occurs due to long-term diabetes with changing blood glucose levels and has become the most common cause of vision loss worldwide. It has become a severe problem among the working-age group that needs to be solved early to avoid vision loss in the future. Artificial intelligence-based technologies have been utilized to detect and grade diabetic retinopathy at the initial level. Early detection allows for proper treatment and, as a result, eyesight complications can be avoided. The in-depth analysis now details the various methods for diagnosing diabetic retinopathy using blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages. In most trials, fundus images of the retina are used, which are taken using a fundus camera. This survey discusses the basics of diabetes, its prevalence, complications, and artificial intelligence approaches to deal with the early detection and classification of diabetic retinopathy. The research also discusses artificial intelligence-based techniques such as machine learning and deep learning. New research fields such as transfer learning using generative adversarial networks, domain adaptation, multitask learning, and explainable artificial intelligence in diabetic retinopathy are also considered. A list of existing datasets, screening systems, performance measurements, biomarkers in diabetic retinopathy, potential issues, and challenges faced in ophthalmology, followed by the future scope conclusion, is discussed. To the author, no other literature has analyzed recent state-of-the-art techniques considering the PRISMA approach and artificial intelligence as the core. Full article
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33 pages, 1624 KiB  
Review
Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review
by Bolanle Adefowoke Ojokoh, Benjamin Aribisala, Oluwafemi A. Sarumi, Arome Junior Gabriel, Olatunji Omisore, Abiola Ezekiel Taiwo, Tobore Igbe, Uchechukwu Madukaku Chukwuocha, Tunde Yusuf, Abimbola Afolayan, Olusola Babalola, Tolulope Adebayo and Olaitan Afolabi
Big Data Cogn. Comput. 2022, 6(4), 111; https://doi.org/10.3390/bdcc6040111 - 09 Oct 2022
Cited by 5 | Viewed by 6503
Abstract
Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain [...] Read more.
Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain and prevent COVID-19. In this review, we analyzed the key elements of digital contact tracing strategies developed for the prevention and containment of the dreaded epidemic since its outbreak. We carried out a scoping review through relevant studies indexed in three databases, namely Google Scholar, PubMed, and ACM Digital Library. Using some carefully defined search terms, a total of 768 articles were identified. The review shows that 86.32% (n = 101) of the works focusing on contact tracing were published in 2020, suggesting there was an increased awareness that year, increased research efforts, and the fact that the pandemic was given a very high priority by most journals. We observed that many (47.86%, n = 56) of the studies were focused on design and implementation issues in the development of COVID-19 contact tracing systems. In addition, has been established that most of the studies were conducted in 41 countries and that contract tracing app development are characterized by some sensitive issues, including privacy-preserving and case-based referral characteristics. Full article
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23 pages, 6422 KiB  
Article
Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19
by Heba Ismail, Ashraf Khalil, Nada Hussein and Rawan Elabyad
Big Data Cogn. Comput. 2022, 6(3), 99; https://doi.org/10.3390/bdcc6030099 - 16 Sep 2022
Cited by 10 | Viewed by 3053
Abstract
This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public’s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, [...] Read more.
This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public’s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. The produced analytics are then factored by location and time to provide more comprehensive insights that aim to assist the decision-makers and personnel in the educational sector enhance and adapt the educational process during and following the pandemic and looking toward the future. The experimental results for emotion classification show that the Linear Support Vector Classifier (SVC) outperformed other classifiers in terms of overall accuracy, precision, recall, and F-measure of 91%. Moreover, the Logistic Regression classifier outperformed all other classifiers in terms of overall accuracy, recall, an F-measure of 81%, and precision of 83% for aspect classification. In online experiments using UAE COVID-19 education-related data, the analytics show high relevance with the public concerns around the education process that were reported during the experiment’s timeframe. Full article
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17 pages, 1280 KiB  
Article
Impactful Digital Twin in the Healthcare Revolution
by Hossein Hassani, Xu Huang and Steve MacFeely
Big Data Cogn. Comput. 2022, 6(3), 83; https://doi.org/10.3390/bdcc6030083 - 08 Aug 2022
Cited by 57 | Viewed by 8525
Abstract
Over the last few decades, our digitally expanding world has experienced another significant digitalization boost because of the COVID-19 pandemic. Digital transformations are changing every aspect of this world. New technological innovations are springing up continuously, attracting increasing attention and investments. Digital twin, [...] Read more.
Over the last few decades, our digitally expanding world has experienced another significant digitalization boost because of the COVID-19 pandemic. Digital transformations are changing every aspect of this world. New technological innovations are springing up continuously, attracting increasing attention and investments. Digital twin, one of the highest trending technologies of recent years, is now joining forces with the healthcare sector, which has been under the spotlight since the outbreak of COVID-19. This paper sets out to promote a better understanding of digital twin technology, clarify some common misconceptions, and review the current trajectory of digital twin applications in healthcare. Furthermore, the functionalities of the digital twin in different life stages are summarized in the context of a digital twin model in healthcare. Following the Internet of Things as a service concept and digital twining as a service model supporting Industry 4.0, we propose a paradigm of digital twinning everything as a healthcare service, and different groups of physical entities are also clarified for clear reference of digital twin architecture in healthcare. This research discusses the value of digital twin technology in healthcare, as well as current challenges and insights for future research. Full article
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21 pages, 4585 KiB  
Article
Cognitive Networks Extract Insights on COVID-19 Vaccines from English and Italian Popular Tweets: Anticipation, Logistics, Conspiracy and Loss of Trust
by Massimo Stella, Michael S. Vitevitch and Federico Botta
Big Data Cogn. Comput. 2022, 6(2), 52; https://doi.org/10.3390/bdcc6020052 - 12 May 2022
Cited by 10 | Viewed by 4053
Abstract
Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. This work reconstructs how popular and trending posts framed semantically and emotionally COVID-19 vaccines on Twitter. We achieve this by merging natural language processing, cognitive network science [...] Read more.
Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. This work reconstructs how popular and trending posts framed semantically and emotionally COVID-19 vaccines on Twitter. We achieve this by merging natural language processing, cognitive network science and AI-based image analysis. We focus on 4765 unique popular tweets in English or Italian about COVID-19 vaccines between December 2020 and March 2021. One popular English tweet contained in our data set was liked around 495,000 times, highlighting how popular tweets could cognitively affect large parts of the population. We investigate both text and multimedia content in tweets and build a cognitive network of syntactic/semantic associations in messages, including emotional cues and pictures. This network representation indicates how online users linked ideas in social discourse and framed vaccines along specific semantic/emotional content. The English semantic frame of “vaccine” was highly polarised between trust/anticipation (towards the vaccine as a scientific asset saving lives) and anger/sadness (mentioning critical issues with dose administering). Semantic associations with “vaccine,” “hoax” and conspiratorial jargon indicated the persistence of conspiracy theories and vaccines in extremely popular English posts. Interestingly, these were absent in Italian messages. Popular tweets with images of people wearing face masks used language that lacked the trust and joy found in tweets showing people with no masks. This difference indicates a negative effect attributed to face-covering in social discourse. Behavioural analysis revealed a tendency for users to share content eliciting joy, sadness and disgust and to like sad messages less. Both patterns indicate an interplay between emotions and content diffusion beyond sentiment. After its suspension in mid-March 2021, “AstraZeneca” was associated with trustful language driven by experts. After the deaths of a small number of vaccinated people in mid-March, popular Italian tweets framed “vaccine” by crucially replacing earlier levels of trust with deep sadness. Our results stress how cognitive networks and innovative multimedia processing open new ways for reconstructing online perceptions about vaccines and trust. Full article
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29 pages, 2381 KiB  
Article
Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches
by Maha Gharaibeh, Dalia Alzu’bi, Malak Abdullah, Ismail Hmeidi, Mohammad Rustom Al Nasar, Laith Abualigah and Amir H. Gandomi
Big Data Cogn. Comput. 2022, 6(1), 29; https://doi.org/10.3390/bdcc6010029 - 08 Mar 2022
Cited by 26 | Viewed by 10765
Abstract
Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death [...] Read more.
Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions. Full article
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