Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.
Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.
The features of this book include:
A unique and complete focus on applications of machine learning in the healthcare sector.
An examination of how data analysis can be done using healthcare data and bioinformatics.
An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values.
An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.
Author(s): Rashmi Agrawal, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore, Dac-Nhuong Le
Publisher: CRC Press
Year: 2020
Language: English
Pages: 222
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Editors
List of Contributors
Chapter 1 Fundamentals of Machine Learning
1.1 Introduction
1.2 Data in Machine Learning
1.3 The Relationship between Data Mining, Machine Learning, and Artificial Intelligence
1.4 Applications of Machine Learning
1.4.1 Machine Learning: The Expected
1.4.2 Machine Learning: The Unexpected
1.5 Types of Machine Learning
1.5.1 Supervised Learning
1.5.1.1 Supervised Learning Use Cases
1.5.2 Unsupervised Learning
1.5.2.1 Types of Unsupervised Learning
1.5.2.2 Clustering
1.5.2.3 Association Rule
1.5.2.4 Unsupervised Learning Use Case
1.5.3 Reinforcement Learning (RL)
1.6 Conclusion
References
Chapter 2 Medical Information Systems
2.1 Introduction
2.2 Types of Medical Information Systems
2.2.1 General Medical Information Systems
2.2.2 Specific Medical Information Systems
2.3 Types of General Medical Data
2.3.1 Numerical Data
2.3.2 Textual Data
2.3.3 Categorical Data
2.3.4 Imaging Data
2.4 History of Medical Information Systems
2.5 Collection of MIS Data through Various Platforms
2.5.1 Traditional
2.5.2 Electronic
2.6 Diagnosis and Treatment of Disease through MIS Data
2.7 Conclusion
References
Chapter 3 The Role of Metaheuristic Algorithms in Healthcare
3.1 Introduction
3.2 Machine Learning in Healthcare
3.3 Health Information System Framework
3.4 Privacy and Security of Data
3.5 Big Data Analytics in Disease Diagnosis
3.6 The Metaheuristic Algorithm for Healthcare
3.7 Conclusion
References
Chapter 4 Decision Support System to Improve Patient Care
4.1 Introduction
4.2 Related Work
4.3 Feature Selection
4.3.1 Entropy Formula
4.4 Experimental Setup
4.5 Conclusion
References
Chapter 5 Effects of Cell Phone Usage on Human Health and Specifically on the Brain
5.1 Introduction
5.2 Background
5.3 Radiation Produced by a Mobile Phone
5.4 MATLAB Tools
5.4.1 Problem Statement
5.4.2 Research Objective
5.5 State-of-the-Art Research and Technology
5.6 Discussion of Tools
5.7 Methodology
5.7.1 Quantitative Approach
5.7.2 Design Research
5.8 Method of Data Collection
5.8.1 Sampling Technique
5.8.2 Sample Size
5.8.3 Instrument for Data Collection
5.8.4 Research Model
5.9 K-Means Clustering
5.10 Result and Discussion
5.11 Conclusion
References
Chapter 6 Feature Extraction and Bio Signals
6.1 Introduction
6.2 Feature Extraction
6.2.1 Common Spatial Patterns
6.2.2 Adaptive Common Spatial Patterns
6.2.3 Adaptive CSP Patches
6.2.4 Canonical Correlation Analysis
6.2.5 Band Power Features
6.2.6 Adaptive Band Power Features
6.2.7 Time Point Features
6.2.8 Time Points with Adaptive XDAWN
6.3 Feature Selection and its Approaches
6.3.1 Filter Approach
6.3.2 Wrapper Approach
6.4 Conclusion
References
Chapter 7 Comparison Analysis of Multidimensional Segmentation Using Medical Health-Care Information
7.1 Introduction
7.2 Literature Review
7.2.1 Static Structure of Literature Review with Another Research Comparison
7.3 Methodology
7.3.1 Original Result of Image Testing in Binary Transformation
7.3.2 High Dimension Structured Graphs
7.3.2.1 Grab-Cut
7.4 Algorithm
7.5 Result Comparison and Discussion
7.6 Conclusion
Acknowledgments
References
Chapter 8 Deep Convolutional Network Based Approach for Detection of Liver Cancer and Predictive Analytics on Cloud
8.1 Introduction
8.1.1 Types of Liver Diseases
8.2 Medical Images and Deep Learning
8.2.1 Micro-Service Architecture
8.2.2 Integration of NVDIA GPU for Deep Learning on Cloud
8.2.3 Presenting the Sockets and Slots for Processors
8.2.4 Clock Details of Deep Learning Server
8.2.5 Threads for Deep Learning–Based Computations
8.2.6 Available Hard Disk for Use
8.2.7 Memory
8.2.8 Overall Details of Used Computing Environment with Deep Convolutional Networks
8.3 Deep Learning for Liver Diagnosis with the Projected Model
8.4 Proposed Model and Outcomes
8.5 Conclusion
References
Chapter 9 Performance Analysis of Machine Learning Algorithm for Healthcare Tools with High Dimension Segmentation
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.3.1 Proposed Framework
9.3.2 Light Field Toolbox for MATLAB
9.3.3 High Dimensional Light Field Segmentation Method
9.3.4 High Dimensional Structured Graphs
9.4 High Dimension Structured Graphs
9.4.1 Grab-Cut
9.4.2 Image Testing Value
9.4.3 Image Testing Result
9.4.4 Graph Cut Value for B/W Image
9.4.5 Image Testing Value
9.4.6 Image Testing Result
9.5 Algorithm
9.6 Result and Discussion
9.7 Conclusion
9.8 Future Work
Acknowledgment
References
Chapter 10 Patient Report Analysis for Identification and Diagnosis of Disease
10.1 Introduction
10.2 Data Variability
10.2.1 Structured Data
10.2.1.1 Human Generated Data
10.2.1.2 Machine Generated Data
10.2.2 Semi-Structured Data
10.2.3 Unstructured Data
10.2.4 Comparison of Structured, Unstructured Data, and Semi-Structured
10.3 Data Collection of Diseases
10.3.1 EMR Data Collection through eHealth Devices
10.3.2 Semantic Data Extraction from Healthcare Websites
10.3.3 Patient Chatbots
10.3.4 Structured Data
10.3.5 Consistency and Quality of Structured Data
10.4 Predictive Models for Analysis
10.4.1 Regression Techniques
10.4.2 Machine Learning Techniques
10.4.3 Algorithms
10.4.3.1 Naïve Bayes
10.4.3.2 Support Vector Machine
10.4.3.3 Logistic Regression
10.4.3.4 Decision Trees
10.4.4 Use Cases
10.4.4.1 Cleveland Clinic
10.4.4.2 Providence Health
10.4.4.3 Dartmouth Hitchcock
10.4.4.4 Google
10.5 Semi-Structured Data
10.5.1 Semantic Extraction
10.5.2 Web Mantic Extraction
10.5.3 Use Cases
10.6 Unstructured Data
10.6.1 Finding Meaning in Unstructured Data
10.6.2 Extraction of Data
10.6.2.1 Text Extraction
10.6.2.2 Image Extraction
10.6.2.3 Challenges of Data Extraction from PDFs
10.6.2.4 Video Extraction
10.6.2.5 Sound Extraction
10.6.3 Algorithms
10.6.3.1 Natural Language Processing
10.6.3.2 Naïve Bayes
10.6.3.3 Deep Learning
10.6.3.4 Convolutional Neural Network
10.6.3.5 Phenotyping Algorithms
10.6.4 Use Cases
10.7 Conclusion
References
Chapter 11 Statistical Analysis of the Pre- and Post-Surgery in the Healthcare Sector Using High Dimension Segmentation
11.1 Introduction
11.2 Methodology
11.2.1 Sampling Techniques
11.2.2 Sample Data and Size
11.2.3 Light Field Toolbox for MATLAB
11.2.4 High Dimensional Light Field Segmentation Method
11.3 Support Vector Machine (SVM)
11.3.1 4-Dimentional SVM Graphs
11.4 Statistical Technique
11.5 Result and Discussion
11.6 Conclusion
11.7 Future Work
References
Chapter 12 Machine Learning in Diagnosis of Children with Disorders
12.1 Introduction
12.1.1 Down Syndrome (DS)
12.1.2 Sensory Processing Disorder (SPD)
12.1.3 Autism Spectrum Disorder (ASD)
12.1.4 Aims and Organisation
12.2 Existing Tools for Diagnosis of DS, SPD, and ASD
12.2.1 Existing Tools of DS Diagnosis
12.2.2 Existing Tools of SPD Diagnosis
12.2.3 Existing Tools for ASD Diagnosis
12.3 Machine Learning Applied for Diagnosis of DS, SPD, and ASD
12.4 Machine Learning Case Studies of DS, SPD, and ASD
12.4.1 Machine Learning (ML) Case Study for DS
12.4.2 Machine Learning Case Study of SPD
12.4.3 Machine Learning Case Study for ASD
12.5 Conclusion
References
Chapter 13 Forecasting Dengue Incidence Rate in Tamil Nadu Using ARIMA Time Series Model
13.1 Introduction
13.2 Literature Review
13.2.1 Findings
13.3 Methods and Materials
13.3.1 Study Area
13.3.2 Snapshot for Dataset
13.3.3 Proposed Model
13.3.4 Estimate and Develop the Model
13.4 Results and Discussions
13.5 Conclusion
13.6 Acknowledgment
References
Index
