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Health and Public Health Applications for Decision Support Using Machine Learning
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"Health and Public Health Applications for Decision Support Using Machine Learning" is a reprint that explores the intersection of machine learning and health sciences. It presents a collection of research and innovations showcasing how data-driven algorithms can transform patient care, disease diagnosis, and public health management. The reprint covers a wide range of topics, including natural language processing for biomedical relation extraction, ensemble learning for blood glucose level forecasting in diabetes management, machine learning for predicting walking stability and fall risk among the elderly, deep learning for pneumonia-infected lung volume quantification, and more.The reprint also discusses applications in precision medicine, early detection of renal damage, cardiac health monitoring, stress classification for mental health assessment, and early diagnosis of intracranial internal carotid artery stenosis. It emphasizes the role of machine learning in managing health crises, such as COVID-19 detection using ECG, voice, and X-ray systems, and reviews AI models in diagnosing adult-onset dementia disorders.Overall, this reprint aims to inspire researchers and healthcare professionals by showcasing the transformative potential of machine learning in healthcare. It hopes to encourage further research and collaboration to advance healthcare and technological innovations for a healthier future.
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Keywords
- action units
- adult-onset dementia
- Alzheimer’s disease
- artificial intelligence
- artificial neural network
- Atherosclerosis
- audio visual
- blood glucose
- Cardiac health
- ChemProt
- Colonies
- comparison between manual and automated image segmentation
- computerized diagnostic systems
- convolutional neural network
- COVID-19
- COVID-19 detection
- COVID-19 severity assessment
- CPR (chemical–protein relation)
- CVD classification
- data selection
- DDI (drug–drug interaction)
- deep learning
- deep neural network
- diabetes
- Discrimination
- Doppler ultrasound
- ECG
- Émotion
- ensemble learning
- Gait
- GAT (graph-attention network)
- group-based trajectory modeling
- hemodynamic modeling
- Image processing
- infected lung segmentation
- internal carotid artery
- largest Lyapunov exponent (LyE)
- Machine learning
- machine-learning models
- Magnetic Resonance Imaging
- measurement uncertainty
- Monte Carlo method
- movement synergy
- n/a
- neural networks
- neuromuscular control
- overground walking
- petri-plates
- pretrained model
- principal component analysis (PCA)
- quantification of lung disease severity
- relation extraction
- risk assessment tool
- RNN-LSTM
- screening strategy
- self-attention
- Signal processing
- Speech
- Stress
- Stroke
- subclinical renal damage
- T5 (text-to-text transfer transformer)
- time-series forecasting
- transfer learning
- transformer
Links
DOI: 10.3390/books978-3-0365-8548-2Editions