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Biblioteca

MARC Lista (tabellare)
Advances in diagnostics of processes and systems : selected papers from the 14th International Conference on Diagnostics of Processes and Systems (DPS), September 21-23, 2020, Zielona Gora (Poland) / / Jozef Korbicz, Krzysztof Patan, Marcel Luzar, editors
Advances in diagnostics of processes and systems : selected papers from the 14th International Conference on Diagnostics of Processes and Systems (DPS), September 21-23, 2020, Zielona Gora (Poland) / / Jozef Korbicz, Krzysztof Patan, Marcel Luzar, editors
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (VIII, 184 p. 58 illus., 33 illus. in color.)
Disciplina 610.28563
Collana Studies in systems, decision and control
Soggetto topico Artificial intelligence - Medical applications
Artificial intelligence - Engineering applications
ISBN 3-030-58964-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Hybrid health-aware supervisory control framework with a prognostic decision-making -- Reconfiguration of nonlinear faulty systems via linear methods -- Tri-valued evaluation of residuals as a method of addressing the problem of fault compensation effect -- Leader-following formation control for networked multi-agent systems under communication -- Regular approach to additive fault detection in discrete-time linear descriptor systems -- Descriptor principle in residual filter design for strictly Metzler linear systems -- Hierarchical model for testing a distributed computer system -- Diagnostics of rotary vane vacuum pumps using signal processing, analysis and clustering methods -- Neural modelling of steam turbine control stage -- Diagnostic of calfs body temperature by using thermal imaging camera and correction of camera errors -- Intruder detection on mobile phones using keystroke dynamic and application usage patterns -- . Application of deep learning to seizure classification -- Patient managed patient health record based on blockchain technology.
Record Nr. UNINA-9910483591403321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI and analytics for public health : proceedings of the 2020 INFORMS International Conference on Service Science / / Hui Yang, Robin Qiu and Weiwei Chen
AI and analytics for public health : proceedings of the 2020 INFORMS International Conference on Service Science / / Hui Yang, Robin Qiu and Weiwei Chen
Autore Yang Hui
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (473 pages)
Disciplina 610.28563
Collana Springer Proceedings in Business and Economics
Soggetto topico Artificial intelligence - Medical applications
Public health - Decision making
ISBN 3-030-75166-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910523736403321
Yang Hui  
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI and Big Data in Cardiology : A Practical Guide / / edited by Nicolas Duchateau and Andrew P. King
AI and Big Data in Cardiology : A Practical Guide / / edited by Nicolas Duchateau and Andrew P. King
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (IX, 216 p. 56 illus., 55 illus. in color.)
Disciplina 060
Soggetto topico Artificial intelligence - Medical applications
Big data
Soggetto non controllato Internal Medicine
Medical
ISBN 3-031-05071-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- AI and Machine Learning: the Basics -- From Machine Learning to Deep Learning -- Measurement and Quantification -- Diagnosis -- Outcome Prediction -- Quality Control -- AI and Decision Support -- AI in the Real World -- Analysis of Non-imaging Data -- Conclusions.
Record Nr. UNINA-9910720085703321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI and Big Data in Cardiology : A Practical Guide / / edited by Nicolas Duchateau and Andrew P. King
AI and Big Data in Cardiology : A Practical Guide / / edited by Nicolas Duchateau and Andrew P. King
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (IX, 216 p. 56 illus., 55 illus. in color.)
Disciplina 060
Soggetto topico Artificial intelligence - Medical applications
Big data
Soggetto non controllato Internal Medicine
Medical
ISBN 3-031-05071-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- AI and Machine Learning: the Basics -- From Machine Learning to Deep Learning -- Measurement and Quantification -- Diagnosis -- Outcome Prediction -- Quality Control -- AI and Decision Support -- AI in the Real World -- Analysis of Non-imaging Data -- Conclusions.
Record Nr. UNISA-996547948903316
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
AI and Blockchain in Healthcare / / Bipin Kumar Rai, Gautam Kumar, and Vipin Balyan, editors
AI and Blockchain in Healthcare / / Bipin Kumar Rai, Gautam Kumar, and Vipin Balyan, editors
Edizione [First edition.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Descrizione fisica 1 online resource (241 pages)
Disciplina 610.285
Collana Advanced Technologies and Societal Change Series
Soggetto topico Artificial intelligence - Medical applications
Blockchains (Databases)
ISBN 9789819903771
9789819903764
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine Learning for Drug Discovery and Manufacturing -- Knowledge Strategies Influencing on The Epidemiologists Performance of The Qeshm Island’s Health Centers -- Healthcare: In the Era of Blockchain -- Securing Healthcare records using Blockchain: Applications and Challenges -- Authentication Schemes For Healthcare Data Using Emerging Computing Technologies -- Biomedical data classification using fuzzy clustering -- Applications of Machine Learning in healthcare With a Case Study of Lung Cancer Detection Through Deep Learning Approach -- Fetal Health Status Prediction During Labor and Delivery Based on Cardiotocogram Data using Machine and Deep Learning -- Blockchain and AI: Disruptive Digital Technologies in Designing the Potential Growth of Healthcare Industries.
Record Nr. UNINA-9910720098203321
Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI and Blockchain in Healthcare / / Bipin Kumar Rai, Gautam Kumar, and Vipin Balyan, editors
AI and Blockchain in Healthcare / / Bipin Kumar Rai, Gautam Kumar, and Vipin Balyan, editors
Edizione [First edition.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Descrizione fisica 1 online resource (241 pages)
Disciplina 610.285
Collana Advanced Technologies and Societal Change Series
Soggetto topico Artificial intelligence - Medical applications
Blockchains (Databases)
ISBN 9789819903771
9789819903764
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine Learning for Drug Discovery and Manufacturing -- Knowledge Strategies Influencing on The Epidemiologists Performance of The Qeshm Island’s Health Centers -- Healthcare: In the Era of Blockchain -- Securing Healthcare records using Blockchain: Applications and Challenges -- Authentication Schemes For Healthcare Data Using Emerging Computing Technologies -- Biomedical data classification using fuzzy clustering -- Applications of Machine Learning in healthcare With a Case Study of Lung Cancer Detection Through Deep Learning Approach -- Fetal Health Status Prediction During Labor and Delivery Based on Cardiotocogram Data using Machine and Deep Learning -- Blockchain and AI: Disruptive Digital Technologies in Designing the Potential Growth of Healthcare Industries.
Record Nr. UNISA-996547959203316
Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
AI doctor : the rise of artificial intelligence in healthcare : a guide for users, buyers, builders, and investors / / Ronald M. Razmi
AI doctor : the rise of artificial intelligence in healthcare : a guide for users, buyers, builders, and investors / / Ronald M. Razmi
Autore Razmi Ronald M.
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (371 pages) : color illustrations
Disciplina 610.285
Soggetto topico Artificial intelligence - Medical applications
ISBN 1-394-24017-1
1-394-24018-X
1-394-24019-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I Roadmap of AI in Healthcare -- Chapter 1 History of AI and Its Promise in Healthcare -- 1.1 What is AI? -- 1.2 A Classification System for Underlying AI/ML Algorithms -- 1.3 AI and Deep Learning in Medicine -- 1.4 The Emergence of Multimodal and Multipurpose Models in Healthcare -- References -- Chapter 2 Building Robust Medical Algorithms -- 2.1 Obtaining Datasets That are Big Enough and Detailed Enough for Training -- 2.2 Data Access Laws and Regulatory Issues -- 2.3 Data Standardization and Its Integration into Clinical Workflows -- 2.4 Federated AI as a Possible Solution -- 2.5 Synthetic Data -- 2.6 Data Labeling and Transparency -- 2.7 Model Explainability -- 2.8 Model Performance in the Real World -- 2.9 Training on Local Data -- 2.10 Bias in Algorithms -- 2.11 Responsible AI -- References -- Chapter 3 Barriers to AI Adoption in Healthcare -- 3.1 Evidence Generation -- 3.2 Regulatory Issues -- 3.3 Reimbursement -- 3.4 Workflow Issues with Providers and Payers -- 3.5 Medical-Legal Barriers -- 3.6 Governance -- 3.7 Cost and Scale of Implementation -- 3.8 Shortage of Talent -- References -- Chapter 4 Drivers of AI Adoption in Healthcare -- 4.1 Availability of Data -- 4.2 Powerful Computers, Cloud Computing, and Open Source Infrastructure -- 4.3 Increase in Investments -- 4.4 Improvements in Methodology -- 4.5 Policy and Regulatory -- 4.5.1 FDA -- 4.5.2 Other Bodies -- 4.6 Reimbursement -- 4.7 Shortage of Healthcare Resources -- 4.8 Issues with Mistakes, Inefficient Care Pathways, and Non-personalized Care -- References -- Part II Applications of AI in Healthcare -- Chapter 5 Diagnostics -- 5.1 Radiology -- 5.2 Pathology -- 5.3 Dermatology -- 5.4 Ophthalmology -- 5.5 Cardiology -- 5.6 Neurology.
5.7 Musculoskeletal -- 5.8 Oncology -- 5.8.1 Diagnosis and Treatment of Cancer -- 5.8.2 Histopathological Cancer Diagnosis -- 5.8.3 Tracking Tumor Development -- 5.8.4 Prognosis Detection -- 5.9 GI -- 5.10 COVID-19 -- 5.11 Genomics -- 5.12 Mental Health -- 5.13 Diagnostic Bots -- 5.14 At Home Diagnostics/Remote Monitoring -- 5.15 Sound AI -- 5.16 AI in Democratizing Care -- References -- Chapter 6 Therapeutics -- 6.1 Robotics -- 6.2 Mental Health -- 6.3 Precision Medicine -- 6.4 Chronic Disease Management -- 6.5 Medication Supply and Adherence -- 6.6 VR -- References -- Chapter 7 Clinical Decision Support -- 7.1 AI in Decision Support -- 7.2 Initial Use Cases -- 7.3 Primary Care -- 7.4 Specialty Care -- 7.4.1 Cancer Care -- 7.4.2 Neurology -- 7.4.3 Cardiology -- 7.4.4 Infectious Diseases -- 7.4.5 COVID-19 -- 7.5 Devices -- 7.6 End-of-Life AI -- 7.7 Patient Decision Support -- References -- Chapter 8 Population Health and Wellness -- 8.1 Nutrition -- 8.2 Fitness -- 8.3 Stress and Sleep -- 8.4 Population Health and Management -- 8.5 Risk Assessment -- 8.6 Use of Real World Data -- 8.7 Medication Adherence -- 8.8 Remote Engagement and Automation -- 8.9 SDOH -- 8.10 Aging in Place -- References -- Chapter 9 Clinical Workflows -- 9.1 Documentation Assistants -- 9.2 Quality Measurement -- 9.3 Nursing and Clinical Assistants -- 9.4 Virtual Assistants -- References -- Chapter 10 Administration and Operations -- 10.1 Providers -- 10.1.1 Documentation, Coding, and Billing -- 10.1.2 Practice Management and Operations -- 10.1.3 Hospital Operations -- 10.2 Payers -- 10.2.1 Payer Administrative Functions -- 10.2.2 Fraud -- 10.2.3 Personalized Communications -- References -- Chapter 11 AI Applications in Life Sciences -- 11.1 Drug Discovery -- 11.2 Clinical Trials -- 11.2.1 Information Engines -- 11.2.2 Patient Stratification -- 11.2.3 Clinical Trial Operations.
11.3 Medical Affairs and Commercial -- References -- Part III The Business Case for AI in Healthcare -- Chapter 12 Which Health AI Applications Are Ready for Their Moment? -- 12.1 Methodology -- 12.2 Clinical Care -- 12.3 Administrative and Operations -- 12.4 Life Sciences -- References -- Chapter 13 The Business Model for Buyers of Health AI Solutions -- 13.1 Clinical Care -- 13.2 Administrative and Operations -- 13.3 Life Sciences -- 13.4 Guide for Buyer Assessment of Health AI Solutions -- References -- Chapter 14 How to Build and Invest in the Best Health AI Companies -- 14.1 Barriers to Entry and Intellectual Property (IP) -- 14.1.1 Creating Defensible Products -- 14.2 Startups Versus Large Companies -- 14.3 Sales and Marketing -- 14.4 Initial Customers -- 14.5 Direct-to-Consumer (D2C) -- 14.6 Planning Your Entrepreneurial Health AI Journey -- 14.7 Assessment of Companies by Investors -- 14.7.1 Key Areas to Explore for a Health AI Company for Investment -- References -- Index -- EULA.
Altri titoli varianti Artificial intelligence doctor
Record Nr. UNINA-9910840779503321
Razmi Ronald M.  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI for disease surveillance and pandemic intelligence : intelligent disease detection in action / / Arash Shaban-Nejad, Martin Michalowski, Simone Bianco, editors
AI for disease surveillance and pandemic intelligence : intelligent disease detection in action / / Arash Shaban-Nejad, Martin Michalowski, Simone Bianco, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Descrizione fisica 1 online resource (335 pages)
Disciplina 610.28563
Collana Studies in computational intelligence
Soggetto topico Artificial intelligence - Medical applications
Public health surveillance
Artificial intelligence
ISBN 3-030-93080-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910552722303321
Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI for healthcare with Keras and Tensorflow 2.0 : design, develop, and deploy machine learning models using healthcare data / / Anshik
AI for healthcare with Keras and Tensorflow 2.0 : design, develop, and deploy machine learning models using healthcare data / / Anshik
Autore Anshik
Pubbl/distr/stampa [Place of publication not identified] : , : Apress, , [2021]
Descrizione fisica 1 online resource (391 pages)
Disciplina 610.285
Soggetto topico Artificial intelligence - Medical applications
ISBN 1-4842-7086-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Introduction -- Chapter 1: Healthcare Market: A Primer -- Different Stakeholders of the Healthcare Marketplace -- Regulators -- Food and Drug Administration (FDA) -- Center for Medicare and Medicaid Services (CMS) -- Center for Medicare and Medicaid Innovation (CMMI) -- Payers -- Providers -- Regulation of Healthcare Information -- AI Applications in Healthcare -- Screening -- Diagnosis -- Prognosis -- Response to Treatment -- What Is the Industry Landscape? -- Conclusion -- Chapter 2: Introduction and Setup -- Introduction to TensorFlow 2 -- TensorFlow Core -- TensorFlow JS -- TensorFlow Lite -- TensorFlow Extended -- TensorFlow 1.x vs 2.x -- What Is TF 1.x? -- Embracing TF 2.x -- Eager Execution -- AutoGraph -- TensorFlow Datasets -- tf.keras -- Estimators -- Recommendations for Best Use -- Installation and Setup -- Python Installation -- Using the Virtual Environment -- Library and Versions -- TensorFlow and GPU -- Others -- Conclusion -- Chapter 3: Predicting Hospital Readmission by Analyzing Patient EHR Records -- What Is EHR Data? -- MIMIC 3 Data: Setup and Introduction -- Access -- Introduction and Setup -- Data -- Social and Demographic -- Admissions Related -- Patient's Clinical Data -- Lab Events -- Comorbidity Score -- Modeling for Patient Representation -- A Brief Introduction to Autoencoders -- Feature Columns in TensorFlow -- Creating an Input Pipeline Using tf.data -- Creating Feature Columns -- Building a Stacked Autoencoder -- Cohort Discovery -- What Is an Ideal Cohort Set? -- Optimizing K-Means Performance -- Deciding the Number of Clusters by Inertia and Silhouette Score Analysis -- Checking Cluster Health -- Multitask Learning Model -- What Is Multitask Learning ? -- Different Ways to Train a MTL Model -- Training Your MTL Model -- Conclusion.
Chapter 4: Predicting Medical Billing Codes from Clinical Notes -- Introduction -- Data -- NOTEEVENTS -- DIAGNOSES_ICD -- Understanding How Language Modeling Works -- Paying Attention -- Transforming the NLP Space: Transformer Architecture -- Positional Encoding -- Multi-Head Attention -- BERT: Bidirectional Encoder Representations from Transformers -- Input -- Token Embeddings -- Segment Embeddings -- Training -- Masked Language Modeling -- Next-Sentence Prediction -- Modeling -- BERT Deep-Dive -- What Does the Vocabulary Actually Contain? -- Training -- Conclusion -- Chapter 5: Extracting Structured Data from Receipt Images Using a Graph Convolutional Network -- Data -- Mapping Node Labels to OCR Output -- Node Features -- Hierarchical Layout -- Line Formation -- Graph Modeling Algorithm -- Input Data Pipeline -- What Are Graphs and Why Do We Need Them? -- Graph Convolutional Networks -- Convolutions over Graph -- Understanding GCNs -- Layer Stacking in GCNs -- Training -- Modeling -- Train-Test Split and Target Encoding -- Creating Flow for Training in StellarGraph -- Training and Model Performance Plots -- Conclusion -- Chapter 6: Handling Availability of Low-Training Data in Healthcare -- Introduction -- Semi-Supervised Learning -- GANs -- Autoencoders -- Transfer Learning -- Weak Supervised Learning -- Exploring Snorkel -- Data Exploration -- Introduction -- Labeling Functions -- Regex -- Syntactic -- Distance Supervision -- Pipeline -- Writing Your LFs -- Working with Decorators -- Preprocessor in Snorkel -- Training -- Evaluation -- Generating the Final Labels -- Conclusion -- Chapter 7: Federated Learning and Healthcare -- Introduction -- How Does Federation Learning Work? -- Types of Federated Learning -- Horizontal Federated Learning -- Vertical Federated Learning -- Federated Transfer Learning -- Privacy Mechanism -- Secure Aggregation.
Differential Privacy -- TensorFlow Federated -- Input Data -- Custom Data Load Pipeline -- Preprocessing Input Data -- Creating Federated Data -- Federated Communications -- Evaluation -- Conclusion -- Chapter 8: Medical Imaging -- What Is Medical Imaging? -- Image Modalities -- Data Storage -- Dealing with 2-D and 3-D Images -- Handling 2-D Images -- DICOM in Python -- EDA on DICOM Metadata -- View Position -- Age -- Sex -- Pixel Spacing -- Mean Intensity -- Handling 3-D Images -- NIFTI Format -- Introduction to MRI Image Processing -- Non-Even Pixel Distribution -- Correlation Test -- Cropping and Padding -- Image Classification on 2-D Images -- Image Preprocessing -- Histogram Equalization -- Isotropic Equalization of Pixels -- Model Creation -- Preparing Input Data -- Training -- Image Segmentation for 3-D Images -- Image Preprocessing -- Bias Field Correction -- Removing Unwanted Slices -- Model Creation -- Preparing Input Data -- Training -- Performance Evaluation -- Transfer Learning for Medical Images -- Conclusion -- References -- Chapter 9: Machines Have All the Answers, Except What's the Purpose of Life -- Introduction -- Getting Data -- Designing Your Q& -- A -- Retriever Module -- Query Paraphrasing -- Retrieval Mechanics -- Term/Phrase-Based -- Semantic-Based -- Reranking -- Comprehension -- BERT for Q& -- A -- Fine-Tuning a Q& -- A Dataset -- Final Design and Code -- Step 0: Preparing the Document Data -- Step 1: BERT-QE Expansion -- Step 1.1: Extract the Top k Documents for a Query Using BM-25 -- Step 1.2: Relevance Score on the Top 200 Documents -- Step 2: Semantic Passage Retrieval -- Step 3: Passage Reranking Using a Fine-Tuned Covid BERT Model on the Med-Marco Dataset -- Step 4: Comprehension -- Conclusion -- Chapter 10: You Need an Audience Now -- Demystifying the Web -- How Does an Application Communicate?.
Cloud Technology -- Docker and Kubernetes -- Why Docker? -- OS Virtualization -- Kubernetes -- Deploying the QnA System -- Building a Flask Structure -- Deep Dive into app.py -- Understanding index.html -- Dockerizing Your Application -- Creating a Docker Image -- Base Image and FROM Command -- COPY and EXPOSE -- WORKDIR, RUN, and CMD -- Dockerfile -- Building Docker Image -- Making It Live Using Heroku -- Conclusion -- Index.
Record Nr. UNINA-9910488719203321
Anshik  
[Place of publication not identified] : , : Apress, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI for the good : artificial intelligence and ethics / / Stefan H. Vieweg, editor
AI for the good : artificial intelligence and ethics / / Stefan H. Vieweg, editor
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (xiv, 256 pages) : illustrations
Disciplina 610.28563
Collana Management for professionals
Soggetto topico Artificial intelligence - Medical applications
ISBN 3-030-66913-0
9783030669133
3030669130
9783030669126
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Artificial intelligence for the good
Record Nr. UNINA-9910484746903321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui