Application of Deep Learning and Convolution Neural Networks for Social Healthcare

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 5617

Special Issue Editors


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Department of Electrical Engineering and of Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: pattern recognition and computer vision; medical imaging; applications for AI; approximate computing; parallelisation on multi-CPU/GPU systems
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Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: AI; machine learning and big data analytics with applications to data signals; 2D and 3D image and video processing and analysis
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Department of Computer Science and Media Technology, Linnaeus University, 351 95 Växjö, Sweden
Interests: resilient cyber-physical systems; homeland security; performability modeling; safe autonomy; intelligent transportation
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Guest Editor
Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: artificial intelligence; machine learning; deep learning; medical imaging; precision medicine; radiomics; multimodal learning; decision support systems; federated learning; smart devices
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Special Issue Information

Dear Colleagues,

Today, data collection and analysis are becoming increasingly important in a variety of application domains as novel technologies advance. The healthcare domain is one of the research fields that has benefitted the most from the new availability of big sources of data and recent successes in automated data processing, becoming one of the most known and active producers of digital information. Different studies show how the quantity of produced electronic data is increasing over time, with some recent estimates indicating it to exceed the Yottabyte size within the next years.

If the growing amount of available data will help the design of effective disease prevention and therapies’ assessment procedures, it may result in an increased effort that physicians have to undergo to perform a diagnosis. The task is made even more difficult by high inter/intra patient variability, the availability of different image acquisition techniques and the need of being able to take into account media coming from different sensors and sources. Moreover, the rise of software-based tools for diagnosis and prognosis working with multimodal data (such as genomics, radiomics, and proteomics, to name a few) is opening up more and more opportunities.

To face this problem, physicians make use of several tools that assist in the analysis of biomedical data, including both those directly aimed at processing data, such as CAD (computer-aided detection and diagnosis), text miners, etc., and those implicitly supporting the research, such as federated learning, edge computing, etc. Among these, artificial intelligence is without any doubt what has been mostly supporting the recent increase in medical data processing and analysis, with artificial neural networks, including both shallow and deep architectures, playing a leading role.

Thus, the aim of this Special Issue is to gather recent advances in the application of deep learning and convolution neural networks (CNN) for social healthcare, to help advance scientific research within the broad field of medical-data-using machine learning, deep learning and big data techniques. In particular, the aim is to analyze how these techniques can be applied to the entire medical processing chain, from acquisition to retrieval, detection and disease prediction. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Medical image processing (e.g., classification, segmentation, etc.);
  • Bit-omics (e.g., radiomics, genomics, etc.);
  • Text mining (e.g., automatic clinical records parsing and analysis);
  • Precision and personalised medicine in healthcare;
  • Drug discovery and protein analysis (e.g., gene analysis);
  • Federated learning (e.g., multi-centre training);
  • Trustworthy AI in medicine (e.g. privacy, fairness and equity in medical data processing);
  • Secure storage and processing of medical data (including attack and defence strategy);
  • Adversarial attacks and poisoning in medical data;
  • Information systems ;
  • Information retrieval;
  • Computer-aided diagnosis (CAD);
  • Digital health records (e.g., clinical data processing and storage);
  • Multimodal and multiple sources Learning (e.g., multi-protocol processing);
  • Green healthcare (e.g. CO2 footprint reduction in medical data processing);
  • Synthetic data generation and data augmentation in medicine;
  • Social media analysis for social wellness (e.g., rumours, fake news, sentiment analysis about drugs, etc.);
  • Long-life learning;
  • Edge-computing in medical domain;
  • Wearable devices, sensors and their analytics.

We look forward to receiving your contributions.

Dr. Stefano Marrone
Dr. Paolo Soda
Dr. Francesco Flammini
Dr. Ermanno Cordelli
Guest Editors

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Keywords

  • medical data mining
  • federated learning
  • trustworthy AI
  • multimodal learning
  • synthetic data
  • precision medicine

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Published Papers (3 papers)

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Research

21 pages, 1753 KiB  
Article
Explainable Deep Learning for COVID-19 Vaccine Sentiment in Arabic Tweets Using Multi-Self-Attention BiLSTM with XLNet
by Asmaa Hashem Sweidan, Nashwa El-Bendary, Shereen A. Taie, Amira M. Idrees and Esraa Elhariri
Big Data Cogn. Comput. 2025, 9(2), 37; https://doi.org/10.3390/bdcc9020037 - 10 Feb 2025
Viewed by 805
Abstract
The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment [...] Read more.
The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment analysis resources for the Arabic language. This paper proposes an explainable Deep Learning (DL) approach designed for sentiment analysis of Arabic tweets related to COVID-19 vaccinations. The proposed approach utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network with Multi-Self-Attention (MSA) mechanism for capturing contextual impacts over long spans within the tweets, while having the sequential nature of Arabic text constructively learned by the BiLSTM model. Moreover, the XLNet embeddings are utilized to feed contextual information into the model. Subsequently, two essential Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), have been employed for gaining further insights into the features’ contributions to the overall model performance and accordingly achieving reasonable interpretation of the model’s output. Obtained experimental results indicate that the combined XLNet with BiLSTM model outperforms other implemented state-of-the-art methods, achieving an accuracy of 93.2% and an F-measure of 92% for average sentiment classification. The integration of LIME and SHAP techniques not only enhanced the model’s interpretability, but also provided detailed insights into the factors that influence the classification of emotions. These findings underscore the model’s effectiveness and reliability for sentiment analysis in low-resource languages such as Arabic. Full article
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24 pages, 2138 KiB  
Article
A Multimodal Machine Learning Model in Pneumonia Patients Hospital Length of Stay Prediction
by Anna Annunziata, Salvatore Cappabianca, Salvatore Capuozzo, Nicola Coppola, Camilla Di Somma, Ludovico Docimo, Giuseppe Fiorentino, Michela Gravina, Lidia Marassi, Stefano Marrone, Domenico Parmeggiani, Giorgio Emanuele Polistina, Alfonso Reginelli, Caterina Sagnelli and Carlo Sansone
Big Data Cogn. Comput. 2024, 8(12), 178; https://doi.org/10.3390/bdcc8120178 - 3 Dec 2024
Viewed by 1031
Abstract
Hospital overcrowding, driven by both structural management challenges and widespread medical emergencies, has prompted extensive research into machine learning (ML) solutions for predicting patient length of stay (LOS) to optimize bed allocation. While many existing models simplify the LOS prediction problem to a [...] Read more.
Hospital overcrowding, driven by both structural management challenges and widespread medical emergencies, has prompted extensive research into machine learning (ML) solutions for predicting patient length of stay (LOS) to optimize bed allocation. While many existing models simplify the LOS prediction problem to a classification task, predicting broad ranges of hospital days, an exact day-based regression model is often crucial for precise planning. Additionally, available data are typically limited and heterogeneous, often collected from a small patient cohort. To address these challenges, we present a novel multimodal ML framework that combines imaging and clinical data to enhance LOS prediction accuracy. Specifically, our approach uses the following: (i) feature extraction from chest CT scans via a convolutional neural network (CNN), (ii) their integration with clinically relevant tabular data from patient exams, refined through a feature selection system to retain only significant predictors. As a case study, we applied this framework to pneumonia patient data collected during the COVID-19 pandemic at two hospitals in Naples, Italy—one specializing in infectious diseases and the other general-purpose. Under our experimental setup, the proposed system achieved an average prediction error of only three days, demonstrating its potential to improve patient flow management in critical care environments. Full article
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17 pages, 1733 KiB  
Article
An Improved Deep Learning Framework for Multimodal Medical Data Analysis
by Sachin Kumar and Shivani Sharma
Big Data Cogn. Comput. 2024, 8(10), 125; https://doi.org/10.3390/bdcc8100125 - 29 Sep 2024
Cited by 5 | Viewed by 2433
Abstract
Lung disease is one of the leading causes of death worldwide. This emphasizes the need for early diagnosis in order to provide appropriate treatment and save lives. Physicians typically require information about patients’ clinical symptoms, various laboratory and pathology tests, along with chest [...] Read more.
Lung disease is one of the leading causes of death worldwide. This emphasizes the need for early diagnosis in order to provide appropriate treatment and save lives. Physicians typically require information about patients’ clinical symptoms, various laboratory and pathology tests, along with chest X-rays to confirm the diagnosis of lung disease. In this study, we present a transformer-based multimodal deep learning approach that incorporates imaging and clinical data for effective lung disease diagnosis on a new multimodal medical dataset. The proposed method employs a cross-attention transformer module to merge features from the heterogeneous modalities. Then unified fused features are used for disease classification. The experiments were performed and evaluated on several classification metrics to illustrate the performance of the proposed approach. The study’s results revealed that the proposed method achieved an accuracy of 95% in terms of accurate classification of tuberculosis and outperformed other traditional fusion methods on multimodal tuberculosis data used in this study. Full article
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