http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Artificial intelligence in medical sciences and psychology
with application of machine language, computer vision, and NLP techniques
1 online resource (178 pages) : illustrations (chiefly color)
Get started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers. What You Will Learn Apply artificial neural networks when modelling medical data Know the standard method for Markov decision making and medical data simulation Understand survival analysis methods for investigating data from a clinical trial Understand medical record categorization Measure personality differences using psychological models Who This Book Is For Machine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work setting.
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: An Introduction to Artificial Intelligence in Medical Sciences and Psychology -- Context of the Book -- The Book's Central Point -- Artificial Intelligence Subsets Covered in this Book -- Structure of the Book -- Tools Used in This Book -- Python Distribution Package -- Anaconda Distribution Package -- Jupyter Notebook -- Python Libraries -- Encapsulating Artificial Intelligence -- Implementing Algorithms -- Supervised Algorithms -- Unsupervised Algorithms -- Artificial Neural Networks -- Conclusion
Chapter 2: Realizing Patterns in Diseases with Neural Networks -- Classifying Cardiovascular Disease Diagnosis Outcome Data by Executing a Deep Belief Network -- Preprocessing the Cardiovascular Disease Diagnosis Outcome Data -- Debunking Deep Belief Networks -- Designing the Deep Belief Network -- Relu Activation Function -- Sigmoid Activation Function -- Training the Deep Belief Network -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation
Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Classifying Diabetes Diagnosis Outcome Data by Executing a Deep Belief Network -- Executing a Deep Belief Network to Classify Diabetes Diagnosis Outcome Data -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Conclusion
Chapter 3: A Case for COVID-19: Considering the Hidden States and Simulation Results -- Executing the Hidden Markov Model -- Descriptive Analysis -- Carrying Out the Gaussian Hidden Markov Model -- Considering the Hidden States in US Confirmed COVID-19 Cases with the Gaussian Hidden Markov Model -- Simulating US Confirmed COVID-19 Cases with the Monte Carlo Simulation Method -- US Confirmed COVID-19 Cases Simulation Results -- Conclusion -- Chapter 4: Cancer Segmentation with Neural Networks -- Exploring Cancer -- Exploring Skin Cancer
Classifying Patient Skin Cancer Outcomes by Executing a CNN -- A CNN Pipeline -- A CNN's Architectural Structure -- Classifying Skin Cancer Diagnosis Image Data by Executing a CNN -- Preprocessing the Training Skin Cancer Image Data -- Preprocessing the Validation Skin Cancer Image Data -- Generating the Training Skin Cancer Diagnosis Image Data -- Tuning the Training Skin Cancer Image Data -- Executing the CNN to Classify Skin Cancer Diagnosis Image Data -- Considering the CNN's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation