دانلود کتاب یادگیری ماشین در مراقبت‌های بهداشتی، پیشرفت‌ها و چشم‌اندازهای آینده، ویرایش اول

Machine Learning in Healthcare: Advances and Future Prospects is an in-depth exploration of the adoption of machine learning techniques in healthcare. The book comprises several chapters, each focusing on a distinct area of implementing machine learning to enhance disease diagnosis, treatment, and health management. Chapter 1 introduces machine learning paradigms, including supervised, unsupervised, and reinforcement learning, emphasizing their critical role in healthcare decision-support systems. Key algorithms such as Naive Bayes, Decision Trees, and Convolutional Neural Networks are examined for their effectiveness in diagnosing diseases such as breast cancer, diabetes, and Alzheimer’s disease. Chapter 2 explores machine learning-based cancer detection and therapy, while Chapter 3 focuses on machine learning methods for the detection and treatment of cardiovascular diseases, highlighting the use of Random Forest, Naive Bayes, and Decision Trees algorithms. Chapter 4 describes an architecture for monitoring thyroid patient health status using machine learning techniques, including data-gathering methods, proposed feature models, and classifier approaches. Chapter 5 examines machine learning applications in smart wearable devices, covering use cases such as sleep health, seizure detection, stress detection, hydration monitoring, diabetic monitoring, and arrhythmia detection. For predicting and treating diabetes, Chapter 6 reviews machine learning algorithms such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Logistic Regression. Chapter 7 delves into the application of machine learning and deep learning approaches to mental health conditions, including depression, schizophrenia, anxiety, and bipolar disorder, using EEG characteristics, cognitive testing, and structural neuroimaging. Finally, Chapter 8 discusses machine learning algorithms for electronic health record (EHR) phenotyping, addressing challenges such as temporality, label absence, and multimodality in EHR data. Machine Learning in Healthcare: Advances and Future Prospects serves as a comprehensive resource for researchers, practitioners, and industry experts working at the intersection of machine learning and healthcare.