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Applications of Machine Learning in Digital Healthcare
| Applications of Machine Learning in Digital Healthcare |
| Autore | Hernandez Silveira Miguel |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Institution of Engineering & Technology, 2022 |
| Descrizione fisica | 1 online resource (372 pages) |
| Disciplina | 610.285631 |
| Altri autori (Persone) | AngSu-Shin |
| Collana | Healthcare Technologies Series |
| Soggetto topico |
Machine learning
Medical care - Data processing |
| ISBN |
9781523155378
152315537X 9781839533365 1839533366 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9911004753803321 |
Hernandez Silveira Miguel
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| Institution of Engineering & Technology, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Machine Learning for Assisted Living / / by Zia Uddin
| Applied Machine Learning for Assisted Living / / by Zia Uddin |
| Autore | Uddin Ziya |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (0 pages) |
| Disciplina |
362.40483
610.285631 |
| Soggetto topico |
Medical informatics
Machine learning User interfaces (Computer systems) Human-computer interaction Health Informatics Machine Learning User Interfaces and Human Computer Interaction |
| ISBN |
9783031115349
3031115341 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1.Assisted Living -- 1. 1. Introduction -- 1.2. Surveys on Assisted Living -- 1.3. Assisted Living Projects -- 1.4. Target Users -- 1.4.1. Indoor Observations -- 1.4.2. Outdoor Observations -- 1.5. Privacy and Data Protection -- 1.6. Conclusion -- References -- 2. Sensors and Features for Assisted Living Technologies -- 2.1. Sensors in User care -- 2.1.1. Wearable Sensors -- 2.1.2. Smart Daily Objects -- 2.1.3. Environmental Sensors -- 2.1.2. Wearables with Ambient Sensors -- 2.1.3. Ambient Sensors in Robotic Assisted Living -- 2.2. Feature Extraction -- 2.2.1. Feature Extraction Using PCA -- 2.2.2. Kernel Principal Component Analysis (KPCA) -- 2.2.3. Feature Extraction Using ICA -- 2.2.4. Linear Discriminant Analysis (LDA) -- 2.2.5. Generalized Discriminant Analysis (GDA) -- 2.3. Discussion -- 2.4. Conclusion -- References -- 3. Machine Learning -- 3.1 Shallow Machine Learning -- 3.1.1. Support Vector Machines -- vii -- 3.1.2. Random Forests -- 3.1.3. AdaBoost and Gradient Boosting -- 3.1.4. Nearest Neighbors -- 3.1.5. Examples -- 3.2. Deep Machine Learning -- 3.2.1. Deep Belief Networks (DBN) -- 3.2.2. Convolutional Neural Network -- 3.2.3. Recurrent Neural Networks -- 3.2.4. Neural Structured Learning -- 3.2.4. Pre-trained deep learning models -- 3.3. Explainable AI (XAI) -- 3.3.1. Local Explanations -- 3.3.2. Rule-based Explanations -- 3.3.3. Visual Explanations -- 3.3.4. Feature Relevance Explanations -- 3.4. Discussion -- 3.5. Conclusion -- References -- 4. Applications -- 4.1. Wearable Sensor-based Behavior Recognition -- 4.1.1. MHEALTH Dataset -- 4.1.2. Experimental Results on MHEALTH Dataset -- 4.1.3. PUC-Rio Dataset -- 4.1.4. Experimental Results on PUC-Rio Dataset -- 4.1.5. ARem Dataset -- 4.1.6. Experimental Results on AReM Dataset -- 4.3. Video Camera-based Behavior Recognition -- 4.3.1. Binary Silhouettes and Features -- 4.3.2. Depth Silhouettes and Features -- 4.3.3. 3-D Model-based HAR -- 4.4. Other Ambient Sensor-based Behavior Recognition -- 4.4.1. CASAS Dataset -- viii -- 4.4.2. Experimental Results -- 4.5. Conclusion -- References. |
| Record Nr. | UNINA-9910590058603321 |
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Uddin Ziya
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Augmentation, Labelling, and Imperfections : Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / / edited by Hien V. Nguyen, Sharon X. Huang, Yuan Xue
| Data Augmentation, Labelling, and Imperfections : Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / / edited by Hien V. Nguyen, Sharon X. Huang, Yuan Xue |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (134 pages) |
| Disciplina |
616.0754
610.285631 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Computers Application software Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computing Milieux Computer and Information Systems Applications |
| ISBN |
9783031170270
303117027X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging -- DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images -- Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study -- Lesser of Two Evils Improves Learning in the Context of Cortical Thickness Estimation Models - Choose Wisely -- TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation -- Disentangling A Single MR Modality -- CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation -- Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning -- CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants -- A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data -- Efficient Medical Image Assessment via Self-supervised Learning -- Few-ShotLearning Geometric Ensemble for Multi-label Classification of Chest X-rays. |
| Record Nr. | UNINA-9910595032503321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fundamentals of Machine Learning and Deep Learning in Medicine / / by Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos
| Fundamentals of Machine Learning and Deep Learning in Medicine / / by Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos |
| Autore | Borhani Reza |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (201 pages) |
| Disciplina |
006.31
610.285631 |
| Collana | Medicine Series |
| Soggetto topico |
Internal medicine
Machine learning Internal Medicine Machine Learning Aprenentatge automàtic Intel·ligència artificial Ús terapèutic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-19502-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Mathematical Modeling of Medical Data -- Linear Learning -- Nonlinear Learning -- Multi-Layer Perceptrons -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Generative Adversarial Networks -- Reinforcement Learning. |
| Record Nr. | UNINA-9910631085603321 |
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Borhani Reza
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Critical Internet of Medical Things : Applications and Use Cases / / edited by Fadi Al-Turjman, Anand Nayyar
| Machine Learning for Critical Internet of Medical Things : Applications and Use Cases / / edited by Fadi Al-Turjman, Anand Nayyar |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (267 pages) |
| Disciplina |
610.28563
610.285631 |
| Soggetto topico |
Cooperating objects (Computer systems)
Artificial intelligence Medical informatics Telecommunication Biomedical engineering Cyber-Physical Systems Artificial Intelligence Health Informatics Communications Engineering, Networks Biomedical Engineering and Bioengineering |
| ISBN | 3-030-80928-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- An Introduction to Basic Concepts on Machine Learning, its architecture and framework -- Machine Learning Models and techniques -- Diseases diagnosis and prediction using Machine Learning -- Machine learning for Mobile/e-health, Tele-medical and Remote healthcare networks -- Machine learning in biomedical, Neuro-critical and medical image processing field -- AI, Deep learning and machine learning enabled connected health informatics -- Machine learning enabled smart healthcare system -- Machine learning based efficient health monitoring systems -- Machine learning case study for virus disease Ebola, COVID-19 consequences -- CASE Study: Machine Learning in Medical domain for Cervical Cancer -- Use cases and applications of machine learning in medical domain -- Conclusion. |
| Record Nr. | UNINA-9910522565003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Disease Detection, Prediction, and Diagnosis : Challenges and Opportunities / / edited by Tanupriya Choudhury, Avita Katal
| Machine Learning for Disease Detection, Prediction, and Diagnosis : Challenges and Opportunities / / edited by Tanupriya Choudhury, Avita Katal |
| Autore | Choudhury Tanupriya |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (480 pages) |
| Disciplina | 610.285631 |
| Altri autori (Persone) | KatalAvita |
| Collana | Medicine Series |
| Soggetto topico |
Medicine, Preventive
Health promotion Diseases Diseases - Animal models Machine learning Health Promotion and Disease Prevention Disease Models Machine Learning |
| ISBN | 981-9642-41-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 Introduction to machine learning and Image Processing for disease detection -- Chapter 2 Comparative Study of Various Deep Learning Methods for Prediction of Disease -- Chapter 3 Introduction to deep learning for disease prediction -- Chapter 4 A survey of image classification techniques for the prediction of diseases -- Chapter 5 Prediction of disease related to heart by using different techniques: A survey -- Chapter 6 Automated Plant Disease Diagnosis with Machine Learning -- Chapter 7 Exploring Disease Prediction Techniques through Data Mining: A Comprehensive Overview -- Chapter 8 Detection of Parkinson’s disease using different machine learning techniques: A comparative analysis -- Chapter 9 Kidney Disease Prediction by Machine Learning Techniques -- Chapter 11 Prediction of Diabetes by using the different machine learning algorithms -- Chapter 12 Investigation of Machine Learning Algorithms in detecting Chronic Kidney Disorder -- Chapter 13 Skin Disease Prediction using machine learning techniques -- Chapter 14 A Comparative Study of Different Machine Learning Techniques for Skin Disease Detection -- Chapter 15 Leveraging MLP-Mixer for Improved Melanoma Diagnosis Using Skin Lesion Images -- Chapter 16 Application of AI to detect Brain Tumors -- Chapter 17 Revolutionizing Brain Tumor Detection: Unleashing the Power of Artificial Intelligence -- Chapter 18 Disease detection and diagnosis using artificial intelligence techniques for sustainable economic growth -- Chapter 19 Developing a COVID-19 Prediction Kit Using Machine Learning -- Chapter 20 Plant Disease Detection: Comprehensive Review of Methods and Techniques. |
| Record Nr. | UNINA-9911009334903321 |
|
Choudhury Tanupriya
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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