Artificial intelligence in medical sciences and psychology : with application of machine language, computer vision, and NLP techniques / Tshepo Chris Nokeri.

Format:

Book

Author/Creator:

Nokeri, Tshepo Chris, author.

Language:

English

Subjects (All):

Artificial intelligence.

Automatic data collection systems.

Physical Description:

1 online resource (178 pages)

Place of Publication:

Berkeley, California : Apress L. P., [2022]

Summary:

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.

Contents:

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

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

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

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

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

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

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

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

Reference

Chapter 8: Medical Records Categorization

Medical Records

Categorization with Linear Discriminant 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

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

Index.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781484282175

1484282175

OCLC:

1321789939