About this Research Topic
The use of social networking in the healthcare industry is rapidly growing. The data from these networks can be used to assess various aspects of a patient's health, such as emotional state and stress levels. Patients with conditions like diabetes and abnormal blood pressure often share their experiences and emotions on these sites, offering valuable information and support to others facing similar challenges. They also post opinions on specific drugs, providing insights to other patients. As a result, the healthcare system needs to consider social networking data when monitoring patients' mental health and predicting potential drug side effects. However, the unstructured and unpredictable nature of the information on social network sites presents a challenge for the healthcare industry to effectively extract and analyze it.
Recently, Natural language processing (NLP) and Deep Learning (DL) models have been used to extract information from unstructured social media data. However, there are still challenges in using this data for emotion detection and drug side effect prediction. One challenge is the need for manual labeling of the data, which is time-consuming and difficult for a large amount of textual data. Another challenge is the limitations of the word2vec model, which does not effectively handle the unique language used in social media and fails to represent new words if they are not present in the training data. Traditional ML models may also struggle with the unstructured nature of social media data for emotion detection and analysis. To address these challenges, there is a need for an intelligent system that can automatically label social media data and accurately represent and analyze the labeled data to identify patients' opinions, emotions, and stress levels.
The aim of this Research Topic is to address the areas of advanced deep learning modeling and ontology-based semantic knowledge for social networking data or textual data analysis. These two aspects can help the existing healthcare industry to process and analyze unstructured and noisy textual data for the prediction of emotional status and accrued stress. This Research Topic will explore the new challenges of multitask deep learning models and ontology-based semantic knowledge in social networking data or textual data analysis. High-quality and state-of-the-art research papers on this subject are encouraged to be published in this collection.
The topics of interest for this Research Topic include, but are not limited to:
• Ontology-based data mining for healthcare recommendation systems.
• Fuzzy semantic knowledge for social networking data analysis.
• Semantic knowledge-based textual data analysis in clinical decision support systems.
• Multitask deep learning model with ontology for healthcare monitoring systems.
• Multitask Deep learning-based for textual data representation.
• Ensemble deep learning models for sentiment analysis.
• Multitask Deep learning models for processing electronic medical records.
• Natural language processing in healthcare systems for biomedical data.
• Reinforcement learning and ontology models for evaluation of biomedical data.
• Applications of AI for biomedical data analysis.
• Ontology-based applications in big data analysis.
• Semantic knowledge-based information extraction and information retrieval in healthcare systems.
Recently, Natural language processing (NLP) and Deep Learning (DL) models have been used to extract information from unstructured social media data. However, there are still challenges in using this data for emotion detection and drug side effect prediction. One challenge is the need for manual labeling of the data, which is time-consuming and difficult for a large amount of textual data. Another challenge is the limitations of the word2vec model, which does not effectively handle the unique language used in social media and fails to represent new words if they are not present in the training data. Traditional ML models may also struggle with the unstructured nature of social media data for emotion detection and analysis. To address these challenges, there is a need for an intelligent system that can automatically label social media data and accurately represent and analyze the labeled data to identify patients' opinions, emotions, and stress levels.
The aim of this Research Topic is to address the areas of advanced deep learning modeling and ontology-based semantic knowledge for social networking data or textual data analysis. These two aspects can help the existing healthcare industry to process and analyze unstructured and noisy textual data for the prediction of emotional status and accrued stress. This Research Topic will explore the new challenges of multitask deep learning models and ontology-based semantic knowledge in social networking data or textual data analysis. High-quality and state-of-the-art research papers on this subject are encouraged to be published in this collection.
The topics of interest for this Research Topic include, but are not limited to:
• Ontology-based data mining for healthcare recommendation systems.
• Fuzzy semantic knowledge for social networking data analysis.
• Semantic knowledge-based textual data analysis in clinical decision support systems.
• Multitask deep learning model with ontology for healthcare monitoring systems.
• Multitask Deep learning-based for textual data representation.
• Ensemble deep learning models for sentiment analysis.
• Multitask Deep learning models for processing electronic medical records.
• Natural language processing in healthcare systems for biomedical data.
• Reinforcement learning and ontology models for evaluation of biomedical data.
• Applications of AI for biomedical data analysis.
• Ontology-based applications in big data analysis.
• Semantic knowledge-based information extraction and information retrieval in healthcare systems.
Keywords: Health informatics, Data mining, knowledge extraction, Semantic Knowledge, Deep Learning
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