01145nas 22003853 450 99628885000331620230522213016.02328-6040(OCoLC)49633424(CKB)1000000000220331(CONSER)--2013200302(EXLCZ)99100000000022033120020422a19999999 --- aengur|||||||||||txtrdacontentcrdamediacrrdacarrierFlexible packagingSt. Charles, IL :Independent Pub.,©1999-Deerfield, Ill. :Stagnito Communications Inc.Troy, MI :BNP Media1535-0797 Flexible packaging magazineFlexible packagingPeriodicalsFlexible packagingfast(OCoLC)fst00927263Periodicals.fastFlexible packagingFlexible packaging.658.564Flexible Packaging Association (Washington, D.C.)JOURNAL996288850003316Flexible packaging1925935UNISA10412nam 22006133 450 991100475380332120240521103436.09781523155378152315537X97818395333651839533366(MiAaPQ)EBC30540284(Au-PeEL)EBL30540284(OCoLC)1379438090(NjHacI)9926635638200041(BIP)088213998(CKB)26635638200041(EXLCZ)992663563820004120230516d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplications of Machine Learning in Digital Healthcare1st ed.Institution of Engineering & Technology2022Stevenage :Institution of Engineering & Technology,2023.©2023.1 online resource (372 pages)Healthcare Technologies SeriesPrint version: Hernandez Silveira, Miguel Applications of Machine Learning in Digital Healthcare Stevenage : Institution of Engineering & Technology,c2023 9781839533358 1839533358 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- Index.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.Healthcare Technologies SeriesMachine learningMedical careData processingMachine learning.Medical careData processing.610.285631Hernandez Silveira Miguel1824968Ang Su-Shin1824969MiAaPQMiAaPQMiAaPQBOOK9911004753803321Applications of Machine Learning in Digital Healthcare4392395UNINA