تکنیک‌های یادگیری عمیق برای اطلاعات زیست‌پزشکی و بهداشت

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis Front Matter Copyright Contributors Unified neural architecture for drug, disease, and clinical entity recognition Introduction Method Bi-directional long short-term memory Model architecture Features layer Char BLSTM Word BLSTM layer CRF layer Training and implementation The benchmark tasks Disease NER Drug NER Clinical NER Results and discussion Experiment design Baseline methods Comparison with baseline Comparison with other methods Disease NER Drug NER Feature ablation study Effects of CRF and BLSTM Effects of using fixed word embedding Effect of size of the training data Analysis of learned word embeddings Error analysis Conclusion References Simulation on real time monitoring for user healthcare information Introduction Background Challenges Objectives Scope Motivation Organization Literature review Past researches Proposed model development Design framework Procedures Performance analysis Benefits of proposed model Experimental observations Server-side working environment Privacy policy Experimental comparisons Cost analysis Real-time server-based observations Scalability analysis Monte Carlo simulation-based analysis Simulation of derived framework for load testing based on real-time data Novelty of proposed model Conclusion Acknowledgments References Multimodality medical image retrieval using convolutional neural network Introduction Need for medical image retrieval Machine learning-convolutional neural network General architecture of CNN Convolutional neural network Convolutional layer Pooling layers ReLu layer Softmax layer CBMIR methodology Medical image database LeNet and AlexNet LeNet-5 AlexNet Training of CNN architectures for classification Optimizing the training parameters for CNN learning Training implementation LeNet model training AlexNet model training Medical image retrieval results and discussion Image retrieval by similarity metrics Image retrieval performance metrics Retrieval results: LeNet Retrieval results: AlexNet Summary and conclusion References Further reading A systematic approach for identification of tumor regions in the human brain through HARIS algorithm Introduction The intent of this chapter Image enhancement and preprocessing Adaptive contourlet transform Image preprocessing for skull removal through structural augmentation HARIS algorithm HARIS algorithm objective function-I HARIS algorithm objective function-II Workflow of HARIS algorithm Experimental analysis and results Conclusion Future scope References Further reading Development of a fuzzy decision support system to deal with uncertainties in working posture analysis using rapid upper li ... Introduction RULA method Uncertainties occur in analyzing the working posture using RULA Research methodology MFIS Development of fuzzy DSS Arm and Wrist analysis Step 1: Computation of Total Upper arm score Step 1a: Calculation of Upper arm score Step 1b: Adjustment score Step 2: Computation of Total Lower arm score Step 2a: Calculation of Lower arm score Step 2b: Adjustment score Step 3: Computation of Total Wrist score Step 3a: Calculation of Wrist score Step 3b: Adjustment score Step 4: Wrist twist score Step 5: Evaluation of Posture score A Step 6: Computation of Muscle use score Step 7: Evaluation of Force/load score Step 8: Calculation of Wrist and Arm score Neck, trunk, and leg analysis Step 9: Computation of Total Neck score Step 9a: Calculation of Neck score Step 9b: Adjustment score Step 10: Computation of Total Trunk score Step 10a: Calculation of Trunk score Step 10b: Adjustment score Step 11: Computation of Leg score Step 12: Evaluation of Posture score B Step 13: Evaluation of Neck, Trunk, and Leg score Posture analysis Step 14: Evaluation of RULA score Step 15: Identifying the Action level Selection of most suitable operations associated with the proposed fuzzy DSS using MFIS Analysis of postures of the female workers engaged in Sal leaf plate-making units: A case study Results and discussion Arm and Wrist analysis Neck, trunk, and leg analysis Posture analysis Conclusions Acknowledgments References Short PCG classification based on deep learning Introduction Heart sound analysis Materials and methods Related works Limitation of segmentation Database Overall system design Preprocessing Continuous wavelet transform Convolutional neural network Convolutional layer Pooling layer Fully connected layer CNN-based automatic prediction GoogleNet Training using GoogleNet Performance parameter Result For analyzing individual datasets For analyzing whole datasets Discussion Conclusion References Development of a laboratory medical algorithm for simultaneous detection and counting of erythrocytes and leukocytes in di ... Introduction Blood cells and blood count Manual hemogram Automated hemogram Digital image processing Hough transform Review Materials and methods Results and discussion Future research directions Conclusion Acknowledgments References Deep learning techniques for optimizing medical big data Relationship between deep learning and big data Roles of deep learning and big data in medicine What makes deep learning and big data necessary in medicine? How are deep learning and big data changing the medicine industry? Examples and application of machine learning in medicine Disease identification/diagnosis Personalized treatment Drug discovery Clinical trial research Smart electronic health records Medical big data promise and challenges Promises and challenges Data aggregation challenges Policy and process challenges Management challenges Cloud storage Data accommodation Data personnel Data nature Technology incorporation Medical big data techniques and tools Batch processing technique and tools Apache Hadoop Dryad Talend Open Studio Apache Mahout Pentaho Stream processing tools Storm Splunk Apache Kafka Interactive analysis tools Google Dremel Apache Drilling Existing optimization techniques for medical big data Big data optimization tools for medicine Sonata ECL-Watch Turbo Other big data optimization tools Analyzing big data in precision medicine Subtyping and biomarker discovery Drug repurposing and personalized treatment Biomedical data The increasing number of samples Increasing heterogeneity of captured data Deep learning in medicine Computational methods Disease subtyping and biomarker discovery Drug repurposing and personalized treatments Conclusion References Further reading Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks Introduction to simulation for biomedical signals and images Deep learning for classification of biomedical signals and images Supervised machine learning Deep learning Artificial neural networks Convolutional neural networks Deep learning requires good data Labeled biomedical image data is difficult to obtain Simulation of biological images and signals Simulation can generate large amounts of labeled data System modeling Monte Carlo methods Markov chain Differences between synthetic data and real data Addressing differences between simulated and real data Reality gap bridging techniques System identification Importance sampling for Monte Carlo simulations Adversarial networks Data augmentation Classification of optical coherence tomography images in heart tissues Prediction model example: Classification of OCT images from heart tissues Simulation to generate synthetic OCT images System model example: Interferometry system for OCT System model example: Physics of light propagation through biological tissue Optical characterization of biological tissue Free path Scattering Reflection Absorption and termination of the photon packet Class I and II paths Coordinate systems Verification of simulation results Bridging the reality gap Importance sampling Generative adversarial network (GAN) for system identification Predictive model training and test process Data collection Verification of predictive model results Evaluation of predictive model(s) Conclusion References Deep learning-based histopathological image analysis for automated detection and staging of melanoma Introduction Data description Melanoma detection Epidermis region identification CNN-based nuclei segmentation CNN architecture CNN training Nuclei classification Feature extraction Feature classification Results and discussions Segmentation performance Nuclei classification performance Cell proliferation index calculation Lymph node segmentation Melanoma region identification CNN-based Nuclei Segmentation and Classification PI calculation Results and discussion Conclusions References Potential proposal to improve data transmission in healthcare systems Introduction Telecommunications channels Discrete events Scientific grounding Proposal and objectives Methodology Precoding bit Signal validation by DQPSK modulation Results Discussion Conclusion References Further reading Transferable approach for cardiac disease classification using deep learning Introduction Proposed work Dataset description Arrhythmia Myocardial infarction Atrial fibrillation Methodology Background Recurrent neural network Long short-term memory Gated recurrent unit Residual convolutional neural network Classical machine learning algorithms Feature extraction Classification algorithms Network architecture Recurrent networks Residual convolution neural network Experimental results Train/test split Hyperparameters Evaluation metrics Transferable approach for arrhythmia classification Transferable approach for myocardial infarction classification Transferable approach for atrial fibrillation classification Comparison of the performance for the proposed method against the existing benchmark results Conclusion References Automated neuroscience decision support framework Introduction Psychophysiological measures Neurological data preprocessing Importance of data preprocessing Data preprocessing techniques Software application support for neuroimage processing Related studies Neuroscience decision support framework System design and methodology Datasets Solution implementation Solution evaluation Discussion Conclusion References Diabetes prediction using artificial neural network Introduction State of art Designing and developing the ANN-based model Dataset Implementation Experiments Comparative analysis Summary Index A B C D E F G H I J K L M N O P Q R S T U W Z __Deep Learning Techniques for Biomedical and Health Informatics__ provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing.