Artificial intelligence in medical sciences and psychology : with application of machine language, computer vision, and NLP techniques

Artificial intelligence in medical sciences and psychology with application of machine language, computer vision, and NLP techniques

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 t...

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Detalles Bibliográficos
Otros Autores: Nokeri, Tshepo Chris, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berkeley, California : Apress L. P. [2022]
Materias:
Artificial intelligence.
Automatic data collection systems.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009664712006719
Tabla de Contenidos:
  • 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
  • Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
  • Visible Presence of Breast Cancer
  • Classifying Ultrasound Scans of Breast Cancer Patients by Executing a CNN
  • Preprocessing the Validation Breast Cancer Image Data
  • Generating the Training Breast Cancer Diagnosis Image Data
  • Tuning the Training Breast Cancer Image Data
  • Executing the CNN to Classify Breast Cancer Diagnosis Image Data
  • Considering the CNN's Performance
  • Accuracy Fluctuations Across Epochs in Training and Cross-Validation
  • Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
  • Conclusion
  • Chapter 5: Modeling Magnetic Resonance Imaging and X-Rays by Executing Artificial Neural Networks
  • Brain Tumors
  • MRI Procedure
  • Preprocessing the Training MRI Image Data
  • Preprocessing the Validation MRI Image Data.
  • Generating the Training MRI Image Data
  • Tuning the Training MRI Image Data
  • Executing the CNN to Classify MRI Image Data
  • Considering the CNN's Performance
  • Accuracy Fluctuations Across Epochs in Training and Cross-Validation
  • Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
  • Pneumonia
  • X-Ray Imaging Procedure
  • Classifying X-Rays by Executing a CNN
  • Processing the X-Ray Image Data
  • Generating the Training Chest X-Ray Image Data
  • Preprocessing the Validation Chest X-Ray Image Data
  • Generating the Validation Chest X-Ray Image Data
  • Tuning the Training Chest X-Ray Image Data
  • Executing the CNN to Classify Chest X-Ray Image Data
  • Considering the CNN's Performance
  • Accuracy Fluctuations Across Epochs in Training and Cross-Validation
  • Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
  • Conclusion
  • Chapter 6: A Case for COVID-19 CT Scan Segmentation
  • A Simple Computer Tomography Scan Procedure
  • Preprocessing the Training COVID-19 Data
  • Preprocessing the Validation COVID-19 CT Scan Data
  • Generating the Training COVID-19 CT Scan Data
  • Tuning the Training COVID-19 CT Scan Data
  • Carrying Out the CNN to Classify COVID-19 CT Scan Data
  • Considering the CNN's Performance
  • Accuracy Fluctuations Across Epochs in Training and Cross-Validation
  • Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
  • Conclusion
  • Chapter 7: Modeling Clinical Trial Data
  • Clinical Trials
  • An Overview of Survival Analysis
  • Context of the Chapter
  • Exploring the Nelson-Aalen Additive Model
  • Descriptive Analysis
  • Realizing a Correlation Relationship
  • Outlining the Survival Table
  • Carrying Out the Nelson-Aalen Additive Model.
  • Outlining the Nelson-Aalen Additive Model's Confidence Interval
  • Discerning the Survival Hazard
  • Discerning the Cumulative Survival Hazard
  • Baseline Survival Hazard
  • Conclusion
  • Reference
  • Chapter 8: Medical Records Categorization
  • Medical Records
  • Context of the Chapter
  • Categorization with Linear Discriminant Analysis
  • Descriptive Analysis
  • Preprocessing the Medical Records Data
  • Carrying Out a Regular Expression
  • Carrying Out Word Vectorization
  • Executing the Linear Discriminant Analysis Model to Classify Patients' Medical Records
  • Considering the Linear Discriminant Analysis Model's Performance
  • Conclusion
  • Chapter 9: A Case for Psychology: Factoring and Clustering Personality Dimensions
  • Personality Dimensions
  • Questionnaires
  • Likert Scale
  • Scale Reliability
  • Spearman-Brown Reliability Testing Strategy
  • Carrying Out the Cronbach's Reliability Testing Strategy
  • Carrying Out the Factor Model
  • Carrying Out the Bartlett Sphericity Test
  • Carrying Out the Kaiser-Meyer-Olkin Test
  • Discerning K with a Scree Plot
  • Carrying Out Eigenvalue Rotation
  • Varimax Rotation
  • Discerning Proportional Variance and Cumulative Variances
  • Carrying Out Cluster Analysis
  • Carrying Out Principal Component Analysis
  • Returning K-Means Labels
  • Discerning K-Means Cluster Centers
  • Conclusion
  • Index.

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