Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies
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Maad M. Mijwil
Abstract
Bronchial allergies in children are a frequent respiratory ailment that can have a major negative effect on quality of life. Effective management requires both individualized treatment programs and an early and accurate diagnosis. Artificial intelligence (AI) has become a potent instrument in the medical field, transforming the methods of diagnosis and therapy. In this work, we investigate the use of AI-based methods in the identification and management of pediatric bronchial allergies. AI algorithms are capable of analyzing vast amounts of patient data, including symptoms, medical histories, and results from diagnostic tests, in order to find trends and correlations. AI systems may accurately detect bronchial allergies by comparing symptoms to a database of patterns that have been linked to the condition. This helps medical practitioners make well-informed judgements. AI algorithms can also evaluate patient data to create individualized therapy regimens. A number of criteria are taken into account, including lifestyle choices, the severity of the symptoms, and how the previous therapies worked. AI can improve patient outcomes by making therapeutic recommendations based on this data. AI-enabled wearable health monitors can gather physiological parameter data continually. AI algorithms can track the course of a disease and forecast flare-ups or exacerbations by analyzing this real-time data. This makes it possible to treat bronchial allergies more effectively and with prompt interventions. AI algorithms can also provide pertinent information and recommendations based on patient data to healthcare professionals, acting as decision support tools. Making precise and effective diagnosis and treatment decisions is aided by this. To sum up, AI-based methods have a lot of promise for improving childhood bronchial allergy diagnosis and therapy. Healthcare providers can improve outcomes for children with bronchial allergies by using AI algorithms and wearable health monitoring devices to give more precise diagnoses, individualized treatment plans, and proactive monitoring.
Abstract
Bronchial allergies in children are a frequent respiratory ailment that can have a major negative effect on quality of life. Effective management requires both individualized treatment programs and an early and accurate diagnosis. Artificial intelligence (AI) has become a potent instrument in the medical field, transforming the methods of diagnosis and therapy. In this work, we investigate the use of AI-based methods in the identification and management of pediatric bronchial allergies. AI algorithms are capable of analyzing vast amounts of patient data, including symptoms, medical histories, and results from diagnostic tests, in order to find trends and correlations. AI systems may accurately detect bronchial allergies by comparing symptoms to a database of patterns that have been linked to the condition. This helps medical practitioners make well-informed judgements. AI algorithms can also evaluate patient data to create individualized therapy regimens. A number of criteria are taken into account, including lifestyle choices, the severity of the symptoms, and how the previous therapies worked. AI can improve patient outcomes by making therapeutic recommendations based on this data. AI-enabled wearable health monitors can gather physiological parameter data continually. AI algorithms can track the course of a disease and forecast flare-ups or exacerbations by analyzing this real-time data. This makes it possible to treat bronchial allergies more effectively and with prompt interventions. AI algorithms can also provide pertinent information and recommendations based on patient data to healthcare professionals, acting as decision support tools. Making precise and effective diagnosis and treatment decisions is aided by this. To sum up, AI-based methods have a lot of promise for improving childhood bronchial allergy diagnosis and therapy. Healthcare providers can improve outcomes for children with bronchial allergies by using AI algorithms and wearable health monitoring devices to give more precise diagnoses, individualized treatment plans, and proactive monitoring.
Chapters in this book
- Frontmatter I
- Contents V
- Contributing authors IX
- Synchronizing neural networks, machine learning for medical diagnosis, and patient representation: looping advanced optimization strategies assisting experts for complex mechanisms behind health and disease detection 1
- The future of predictive health: evaluating the role of neural network based hybrid models in healthcare 19
- An overview of new trends on deep learning models for diabetes risk prediction 47
- A study on the detection and diagnosis of cervical cancer using machine and deep learning models 57
- Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques 71
- Combining decision tree and Bayesian networks for improved predictive analytics 91
- Emerging trends in hybrid information systems modeling in artificial intelligence 115
- Hybrid approaches for improving cybersecurity and network intrusion system 153
- IoT security enhancement through blockchain solutions 167
- Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions 177
- Hybrid information systems for modeling traffic management and control 201
- Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach 223
- A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data 241
- Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning 269
- Recent advancements in perfect difference networks for image recognition: a survey and analysis 307
- Image to text to speech: a web-based application using optical character recognition and speech synthesis 329
- Biomimicry and nature-inspired solutions for environmental sustainability 343
- Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0 355
- Harnessing the power of hybrid models for supply chain management and optimization 407
- Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide 427
- Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide 443
- Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide 459
- Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide 473
- Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies 491
- Index 501
Chapters in this book
- Frontmatter I
- Contents V
- Contributing authors IX
- Synchronizing neural networks, machine learning for medical diagnosis, and patient representation: looping advanced optimization strategies assisting experts for complex mechanisms behind health and disease detection 1
- The future of predictive health: evaluating the role of neural network based hybrid models in healthcare 19
- An overview of new trends on deep learning models for diabetes risk prediction 47
- A study on the detection and diagnosis of cervical cancer using machine and deep learning models 57
- Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques 71
- Combining decision tree and Bayesian networks for improved predictive analytics 91
- Emerging trends in hybrid information systems modeling in artificial intelligence 115
- Hybrid approaches for improving cybersecurity and network intrusion system 153
- IoT security enhancement through blockchain solutions 167
- Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions 177
- Hybrid information systems for modeling traffic management and control 201
- Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach 223
- A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data 241
- Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning 269
- Recent advancements in perfect difference networks for image recognition: a survey and analysis 307
- Image to text to speech: a web-based application using optical character recognition and speech synthesis 329
- Biomimicry and nature-inspired solutions for environmental sustainability 343
- Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0 355
- Harnessing the power of hybrid models for supply chain management and optimization 407
- Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide 427
- Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide 443
- Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide 459
- Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide 473
- Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies 491
- Index 501
