Applications of Machine Learning in Digital Healthcare.

Format:

Book

Author/Creator:

Hernandez Silveira, Miguel.

Contributor:

Ang, Su-Shin.

Series:

Healthcare Technologies Series

Language:

English

Subjects (All):

Machine learning.

Medical care--Data processing.

Medical care.

Physical Description:

1 online resource (372 pages)

Edition:

1st ed.

Place of Publication:

Institution of Engineering & Technology 2022

Stevenage : Institution of Engineering & Technology, 2023.

Summary:

This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be applied to help individual patients.

Contents:

Intro

Title

Copyright

Contents

About the editors

1 Introduction

1.1 Why?

1.2 How?

1.3 What is ML?

1.4 The problem

1.5 Gradient descent

1.6 Structural components of the ANN

1.6.1 The fully connected neural network

1.6.2 Convolutional neural network

1.6.3 Pooling layers

1.6.4 The SoftMax function

1.6.5 Putting them together

1.7 Training and evaluating a neural network

1.7.1 Data organisation

1.7.2 Types of errors and useful evaluation metrics

1.7.3 ADAM optimisation for bias reduction

1.7.4 Regularisation for variance reduction

1.8 Conclusion

References

2 Health system planning and optimisation - advancements in the application of machine learning to policy decisions in global health

2.1 Model-based decision making

2.2 ML surrogates for prediction from epidemiological models

2.2.1 Gaussian process regression

2.2.2 Action-value function example

2.2.3 Epidemiological model calibration

2.2.4 Bayesian optimisation

2.3 Online learning

2.3.1 Stochastic multi-armed Bandit

2.4 Running epidemiological simulations as Bandits

2.4.1 Time

2.4.2 State

2.4.3 Action

2.4.4 Reward

2.4.5 Bandit approaches for simulated learning

2.4.6 Extensions to online learning

2.5 Reinforcement learning

2.5.1 State

2.5.2 Action

2.5.3 Reward

2.5.4 Markov decision processes

2.5.5 Cumulated return

2.5.6 Policy

2.5.7 Value function

2.5.8 Partially observable MDP (POMDP)

2.5.9 Learning sequential surrogate models from episodic simulators

2.5.10 Prediction - learning a value function

2.5.11 Simulation-based search - decision trees

2.5.12 Monte Carlo tree search (MCTS)

2.5.13 Gaussian process regression with selection in MCTS for learning sequential surrogates (GP-MCTS)

2.6 Control - optimal policies and planning.

2.6.1 Optimal policy learning

2.7 Comparing predictions from multi-step and one-step methods with direct experience

3 Health system preparedness - coordination and sharing of computation, models and data

3.1 Computation

3.1.1 A proposed infrastructure

3.1.2 Platform components

3.1.3 Performance results

3.1.4 Example: technical approach for competitions

3.1.5 Environment web service

3.1.6 Competition API

3.1.7 Example code

3.1.8 Related work

3.2 ML competitions for health system preparedness

3.3 Planning from learnt models

3.4 KDD Cup 2019 and other competitions

3.4.1 Evaluation framework

3.4.2 Submission and scoring

3.4.3 Other competitions

3.5 Collaboration from competition

3.6 Example: analysis of successful competition approaches

3.6.1 Conclusions on competitions for health system planning

3.6.2 Human-in-the-loop

4 Applications of machine learning for image-guided microsurgery

4.1 Preoperative data collection

4.2 Preprocessing

4.2.1 Intensity histograms

4.2.2 Noise reduction

4.2.3 Contrast adjustment

4.2.4 Preprocessing review

4.3 Segmentation

4.3.1 Thresholding

4.3.2 Region-based thresholding

4.3.3 Edge-based thresholding

4.3.4 Post-processing

4.3.5 Validation

4.4 Registration

4.4.1 Image labeling

4.4.2 Feature identification

4.4.3 Feature matching

4.4.4 Transformation

4.5 Visualization

4.5.1 Real-time motion tracking

4.5.2 Overlaying

4.5.3 Image-guided microscopic surgery system

4.5.4 Augmented-reality-based microsurgical systems

4.6 Challenges

4.6.1 Infrastructure challenges

4.6.2 Safety challenges

4.6.3 Cost challenges

4.7 Chapter review

5 Electrophysiology and consciousness: a review

5.1 Introduction

5.2.1 Central nervous system

5.2.2 ANS.

5.2.3 CNS-ANS connection in physiological mechanisms

5.2 Nervous system signals

5.3 Neurophysiological signal recording

5.3.1 Recording the electroencephalogram (EEG)

5.3.2 Recording the ECG

5.4 Applications of biopotentials in health and disease

5.4.1 Neurodegeneration

5.4.2 Anesthesia

5.4.3 Peri-operative stress

5.5 Analysis tools

5.5.1 ECG analysis

5.5.2 EEG analysis methods

5.5.3 Machine learning methods

5.6 Conclusion

6 Brain networking and early diagnosis of Alzheimer's disease with machine learning

6.1 Background

6.1.1 A brief history of brain study

6.1.2 Modern understanding of the brain

6.2 Laboratory model of brain connectivity

6.3 Problem definition

6.4 Devices used in AD diagnosis

6.5 Data types

6.6 Data preprocessing of MRI data

6.6.1 Median filters

6.6.2 Physiological noise removal by means of deconvolution

6.6.3 Image fusion

6.7 Machine learning for early AD diagnosis

6.7.1 SVMs

6.7.2 Deep learning

6.7.3 SVM techniques

6.7.4 Deep learning techniques

6.8 Conclusion

7 From classic machine learning to deep learning advances in atrial fibrillation detection

7.1 Physiology essentials

7.1.1 The healthy heart

7.1.2 Atrial fibrillation

7.2 Detection of AF

7.2.1 AF detection based on beat-to-beat irregularities

7.2.2 AF detection based on the ECG waveform morphology and hybrid methods

7.3 Conclusions

8 Dictionary learning techniques for left ventricle (LV) analysis and fibrosis detection in cardiac magnetic resonance imaging (MRI)

8.1 Introduction

8.2 Basics of dictionary learning

8.2.1 Probabilistic methods

8.2.2 Clustering-based methods

8.2.3 Parametric training methods

8.3 DL in medical imaging - fibrosis detection in cardiac MRI

8.4 HCM and fibrosis.

8.4.1 Myocardial fibrosis in HCM

8.5 Cardiac magnetic resonance imaging with LGE-MRI

8.6 The assessment of cardiac fibrosis detection in LGE-MRI: a brief state-of-the-art

8.7 The proposed method

8.7.1 Feature extraction

8.7.2 Clustering

8.7.3 DL-based classification: training stage

8.7.4 DL-based classification: testing stage

8.8 First experiments and results

8.8.1 Study population

8.8.2 Results

8.8.3 Evaluation

8.9 Qualification and quantification of myocardial fibrosis: a first proposal

8.10 Conclusion

9 Enhancing physical performance with machine learning

9.1 Introduction

9.2 Physical performance and data science

9.2.1 Physical performance overview

9.2.2 The role of data in physical performance

9.2.3 Why ML?

9.3 Contextualise physical performance factors: ML perspectives

9.3.1 Training

9.3.2 Nutrition

9.3.3 Sleep and recovery

9.4 ML modelling for physical performance problems

9.4.1 Choosing ML models for the right physical performance tasks

9.4.2 Contributing ML features and methods

9.4.3 Challenges

9.5 Limitation

9.6 Conclusion

10 Wearable electrochemical sensors and machine learning for real-time sweat analysis

10.1 Electrochemical sensors: the next generation of wearables

10.2 The mechanisms and content of sweat

10.3 Considerations for on-body sweat analysis

10.3.1 Sweat gland densities and sweat rates

10.3.2 Sweat collection techniques and challenges

10.4 Current trends in wearable electrochemical sweat sensors

10.4.1 Common features of wearable sweat sensors

10.4.2 Opportunities for ISFETs and machine learning in wearable sweat sensing

10.5 The ion-sensitive field-effect transistor

10.5.1 The fundamental theory of ISFETs

10.5.2 ISFETs in CMOS

10.5.3 ISFETs in CMOS for sweat sensing.

10.5.4 Existing ISFET-based wearable sweat sensors

10.6 Applications of machine learning in wearable electrochemical sensors

10.6.1 Existing research into ML for biosensors

10.6.2 Existing research into ML for ISFETs

10.6.3 Integration of analogue classifiers with ISFETs in CMOS

10.7 Summary and conclusions

11 Last words

11.1 Introduction

11.2 A review of the state-of-the-art

11.3 Implementation and deployment

11.3.1 Traditional computing and the memory hierarchy

11.3.2 Graphics processing unit

11.3.3 Hardware accelerators

11.4 Regulatory landscape

11.4.1 A brief interlude

11.4.2 Software development life cycle

11.4.3 Risk management in medical software development

11.4.4 Challenges specific to ML

11.5 Conclusion

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Hernandez Silveira, Miguel Applications of Machine Learning in Digital Healthcare

ISBN:

9781523155378

152315537X

9781839533365

1839533366

OCLC:

1379438090