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The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner
| The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner |
| Autore | Larner A. J. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (175 pages) |
| Disciplina | 519.56 |
| Soggetto topico |
Contingency tables
Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030749200
9783030749194 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910520073503321 |
Larner A. J.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner
| The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner |
| Autore | Larner A. J. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (175 pages) |
| Disciplina | 519.56 |
| Soggetto topico |
Contingency tables
Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030749200
9783030749194 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466554203316 |
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Larner A. J.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advanced prognostic predictive modelling in healthcare data analytics / / Sudipta Roy, Lalit Mohan Goyal, Mamta Mittal, editors
| Advanced prognostic predictive modelling in healthcare data analytics / / Sudipta Roy, Lalit Mohan Goyal, Mamta Mittal, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Artificial intelligence - Medical applications
Medical informatics Information visualization Pronòstic mèdic Simulació per ordinador Intel·ligència artificial en medicina Informàtica mèdica Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0538-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483684603321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in intelligent computing and communication : proceedings of ICAC 2020 ; Bhubaneswar, Odisha, India, November 2020 / / editors, Swagatam Das, Mihir Narayan Mohanty
| Advances in intelligent computing and communication : proceedings of ICAC 2020 ; Bhubaneswar, Odisha, India, November 2020 / / editors, Swagatam Das, Mihir Narayan Mohanty |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (713 pages) : illustrations (chiefly color) |
| Disciplina | 621.382 |
| Collana | Lecture notes in networks and systems |
| Soggetto topico |
Digital communications
Image processing - Digital techniques Soft computing Processament digital d'imatges Intel·ligència artificial en medicina Informàtica mèdica COVID-19 |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-16-0695-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Classification and Detection of Leaves using Different Image processing Techniques Chapter 2. Covid-19 Detection :An Approach Using X-Ray Images and Deep Learning Techniques Chapter 3. Covid-19 Detection :An Approach Using X-Ray Images and Deep Learning Techniques Chapter 4. Realization of a vehicular robotic system using the principle of photonics Chapter 5. A Modified Hybrid Planar Antenna for Cognitive Radio Application Chapter 6. Detection of Broken and Good Medical Tablets Using Various Machine Learning Models Chapter 7. Lungs Nodule Prediction using Convolutional Neural Network and K-Nearest Neighbor Chapter 8. Quantitative Structure Activity Relationships (QSARs) Study for KCNQ Genes(Kv7) and Drug discovery Chapter 9. Apple fruit disease detection and classification using k-means clustering method Chapter 10. A Detailed Review of the Optimal Distributed Generation Placement in Smart Power Distribution Systems |
| Record Nr. | UNINA-9910483988903321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial intelligence in COVID-19 / / Niklas Lidströmer and Yonina C. Eldar, editors
| Artificial intelligence in COVID-19 / / Niklas Lidströmer and Yonina C. Eldar, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (346 pages) |
| Disciplina | 362.1962414 |
| Soggetto topico |
COVID-19 (Disease) - Data processing
Medical informatics Pandemics - Economic aspects COVID-19 Epidèmies Processament de dades Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-08506-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Contents -- About the Editors -- Chapter 1: Introduction to Artificial Intelligence in COVID-19 -- Pandemics -- History of Pandemics -- The COVID-19 Pandemic -- Origins of the COVID-19 Pandemic -- Continuous Fight for Science and Reason -- Modern Tools for Pandemic Control -- A Brief Chronology of the Chapters of This Book -- Power of Science -- References -- Chapter 2: AI for Pooled Testing of COVID-19 Samples -- Introduction -- System Model -- The PCR Process -- Mathematical Model -- Pooled COVID-19 Tests -- Recovery from Pooled Tests -- Group Testing Methods for COVID-19 -- Adaptive GT Methods -- Non-Adaptive GT Methods -- Pooling Matrix -- Noiseless Linear Non-Adaptive Recovery -- Noisy Non-Linear Non-Adaptive Recovery -- Summary -- Compressed Sensing for Pooled Testing for COVID-19 -- Compressed Sensing Forward Model for Pooled RT-PCR -- CS Algorithms for Recovery -- Details of Algorithms -- Assessment of Algorithm Performance and Experimental Protocols -- Choice of Pooling Matrices -- Choice of Number of Pools -- Use of Side Information in Pooled Inference -- Comparative Discussion and Summary -- References -- Chapter 3: AI for Drug Repurposing in the Pandemic Response -- Introduction -- Desirable Features of AI for Drug Repurposing in Pandemic Response -- Technical Flexibility and Efficiency -- Clinical Applicability and Acceptability -- Major AI Applications for Drug Repurposing in Response to COVID-19 -- Knowledge Mining -- Network-Based Analysis -- In Silico Modelling -- IDentif.AI Platform for Rapid Identification of Drug Combinations -- Project IDentif.AI -- IDentif.AI for Drug Optimization Against SARS-CoV-2 -- IDentif.AI 2.0 Platform in an Evolving Pandemic -- IDentif.AI as a Pandemic Preparedness Platform -- Use of Real-World Data to Identify Potential Targets for Drug Repurposing.
Future Directions -- References -- Chapter 4: AI and Point of Care Image Analysis for COVID-19 -- Introduction -- Motivation for Using Imaging -- Motivation for Using AI with Imaging -- Integration of Imaging with Other Modalities -- Literature Overview -- Chest X-Ray Imaging -- Diagnosis Models -- Prognosis Models -- Use of Longitudinal Imaging -- Fusion with Other Data Modalities -- Common Issues with AI and Chest X-Ray Imaging -- Duplication and Quality Issues -- Source Issues -- Frankenstein Datasets -- Implicit Biases in the Source Data -- Artificial Limitations Due to Transfer Learning -- Computed Tomography Imaging -- Diagnosis Models -- Prognosis Models -- Applications to Regions Away from the Lungs -- Use of Longitudinal Imaging -- Fusion with Other Data Modalities -- Common Issues with AI and Computed Tomography Imaging -- Ultrasound Imaging -- What Can be Observed in LUS -- Models Assisting in Interpreting LUS -- Diagnosis Models -- Prognosis Models -- Use of Longitudinal Imaging -- Common Issues with AI and Ultrasound Imaging -- Conclusions -- Success Stories -- Pitfalls to Focus On -- Lessons Learned and Recommendations -- The Next Pandemic -- References -- Chapter 5: Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19 -- Introduction -- COVID-19, Machine Learning and Laboratory Values: The State of the Art -- Literature Search Results -- Diagnostic Studies -- Prognostic Studies -- Considerations on the Literature Reviewed -- Heterogeneity in Patient Selection -- Laboratory Parameters Used by Machine Learning Models -- Types of Models and Their Validation -- Model Implementation -- The Role of Artificial Intelligence in the Vaccination Strategy Against SARS-COV-2 Through Laboratory Tests -- Real-World Vaccination Strategies -- Artificial Intelligence Potentialities -- Conclusions. Appendix 1 -- Diagnostic Papers (D) -- Prognostic Papers (P) -- Appendix 2: Tool Online -- References -- Chapter 6: AI and the Infectious Medicine of COVID-19 -- Introduction -- AI and ML for SARS-CoV-2 Early Research Using Pathogen Sequence Data -- AI and ML for Research of SARS-CoV-2 Antivirals -- AI and ML for COVID-19 Infectious Medicine Early Research Using Language Data -- AI and ML in Real World Data Analysis of COVID-19 -- AI and ML in Molecular Diagnostics of COVID-19 -- AI and ML in Image-Based Diagnostics of COVID-19 and Clinical Decision Support -- AI and ML in COVID-19 Medical Care -- Prevention, Infection Risk and Epidemiology -- Treatment and Prognosis -- Conclusions -- References -- Chapter 7: AI and ICU Monitoring on the Wake of the COVID-19 Pandemic -- Introduction -- ICU Monitoring Through AI -- ICU Monitoring and AI in Pre-pandemic Times -- The Impact of the COVID-19 Pandemic on the ICU and the Role of AI -- Conclusions -- References -- Chapter 8: Symptom Based Models of COVID-19 Infection Using AI -- Introduction -- Using Machine Learning Methods to Determine Mortality of Patient with COVID-19 -- Using Machine Learning Methods to Detect the Presence of COVID-19 Infection -- Using Machine Learning Methods to Differentiate COVID-19 and Influenza/Common Cold Infections -- Summary, Limitations, Challenges, and Future Applications -- References -- Chapter 9: AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice -- Introduction -- A Review of Model Types and Limits to Forecasting -- Preliminaries -- Model Details -- Metrics for Forecast Evaluations -- AI-Driven Engineering -- An Example of a Real-time Forecasting Model -- Results -- A GNN-Based Spatio-Temporal Model -- Additional Details Regarding the Framework -- Forecasting Performance -- Theoretical Foundations for Forecasting in Network Models -- Overview. Some Short-Term Forecasting Problems and Their Computational Intractability -- Discussion -- References -- Chapter 10: Regulatory Aspects on AI and Pharmacovigilance for COVID-19 -- What Does Artificial Intelligence Mean According to Legal Definition? -- AI and Health -- The European Union Legal Framework: A Work in Progress -- The Proposed EU Regulation (Artificial Intelligence Act) -- The Use of AI in Research and Developing Medicinal Products and Monitoring Their Quality, Safety and Efficacy -- The Added Value Brought Using Artificial Intelligence in Performing Pharmacovigilance Activities in General and During the COVID-19 Pandemic -- Ethical Issues: A Few Caveats -- The Personal Data Protection Implications -- Provisional Conclusions -- Suggested Reading -- Chapter 11: AI and the Clinical Immunology/Immunoinformatics for COVID-19 -- Introduction -- Challenge for Traditional Vaccines in COVID-19 -- Long Development and Design Period -- Difficulties in Knowing and Optimizing the Efficacy and Side Effects -- Uncertainties with the Development and Other Costs During Production, Storage, and Transportation -- Hard to Tackle Unknown and Emerging Mutations of Viruses -- Existing AI Techniques Help the Traditional Vaccine Development in COVID-19 -- AI Makes the Practical Experimental Results Computational -- AI-Based Computational Tools Can Help the Traditional Vaccine Design -- AI-Based In Silico Vaccine Design -- Our Recently Proposed DeepVacPred Vaccine Design Framework -- Artificial Intelligence for Investigating Viral Evolution and Mutations -- An Algorithmic Information Theoretic Approach to Discover the State Machine Generator Governing the Viral Sequence Structure and Enabling AI Strategies for Viral Mutation Prediction -- Characterizing the Temporal Evolution of SARS-CoV-2 in a Continuous Manner. Detecting Regions Within Viral Sequences Likely to Exhibit Mutations -- Summary -- References -- Chapter 12: AI and Dynamic Prediction of Deterioration in Covid-19 -- Introduction -- COVID-19: A Novel Disease-Usage of Newer or Older Clinical Decisions Support Systems? -- Clinical Decisions Support System Stable Parameters/Features Using Threshold Values -- Patient Deterioration -- General Prediction Scores -- Early Warning Systems (EWS) -- AI for Prediction of Deterioration -- AI Assisted Patient-Specific Risk Prediction -- AI Assisted Prediction of Critical Illness and Deterioration in COVID-19 Patients -- Mortality Prediction Models for Covid-19 -- Mortality Prediction Models Using High-Frequency Data -- Prediction Models for Sepsis -- Explainable and Interpretable Machine Learning Methods for Clinical Decision Support Systems -- References -- Chapter 13: AI, Epidemiology and Public Health in the Covid Pandemic -- Introduction -- Epidemiology: Definition and Purposes -- Epidemiology and Public Health: How They Relate to Each Other and the Concept of One Health -- Individual Health and Population Health -- The Articulation Between Individual and Population Level -- Biomedical and Biopsychosocial Models of Health: Individual, Environmental and Social Determinants of Health -- From Precision Medicine to Precision Public Health -- Epidemiology and Public Health in the Digital Era: Prerequisites -- A Ubiquitous Digitization -- The Evolutions of the Regulatory Framework on Personal Data -- Connected Devices and Equipment Rates -- Digital and E-health Literacy -- Towards a Real Life Use of AI in Epidemiology and Public Health: Some First Examples -- No Data Means No Artificial Intelligence: A Few Words About Data Federation and "New" Types of Data -- Citizens and Patients as Producers, Actor and Manager of Their Own Health. At the Population Level, Health Surveillance Systems and AI. |
| Record Nr. | UNINA-9910624309003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Basic knowledge of medical imaging informatics : undergraduate level and level I / / Peter M. A. van Ooijen, editor
| Basic knowledge of medical imaging informatics : undergraduate level and level I / / Peter M. A. van Ooijen, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (103 pages) |
| Disciplina | 616.0754 |
| Collana | Imaging Informatics for Healthcare Professionals |
| Soggetto topico |
Radiology
Medical informatics Imatges mèdiques Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-71885-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484385703321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biomedical informatics : computer applications in health care and biomedicine / / editors, Edward H. Shortliffe, James J. Cimino ; section editor, Michael F. Chiang
| Biomedical informatics : computer applications in health care and biomedicine / / editors, Edward H. Shortliffe, James J. Cimino ; section editor, Michael F. Chiang |
| Edizione | [Fifth edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1179 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Medical informatics
Ciències de la salut Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-58721-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484200103321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Clinical Chinese Named Entity Recognition in Natural Language Processing [[electronic resource] /] / by Shuli Guo, Lina Han, Wentao Yang
| Clinical Chinese Named Entity Recognition in Natural Language Processing [[electronic resource] /] / by Shuli Guo, Lina Han, Wentao Yang |
| Autore | Guo Shuli |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (103 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Machine learning
Computational linguistics Clinical medicine—Research Image processing—Digital techniques Computer vision Machine Learning Computational Linguistics Clinical Research Computer Imaging, Vision, Pattern Recognition and Graphics Tractament del llenguatge natural (Informàtica) Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Medicine
Medical |
| ISBN | 981-9926-65-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Theoretical Basis -- Related Existed Models -- Medical Named Entity Recognition Models with the Attention Distraction Mechanism -- Transformer Entity Automatic Extraction Models in Multi-Layer Soft Location Matching Format -- Medical Named Entity Recognition Modelling based on Remote Monitoring and Denoising. |
| Record Nr. | UNINA-9910739465403321 |
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Guo Shuli
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Science and Medical Informatics in Healthcare Technologies [[electronic resource] /] / by Nguyen Thi Dieu Linh, Zhongyu (Joan) Lu
| Data Science and Medical Informatics in Healthcare Technologies [[electronic resource] /] / by Nguyen Thi Dieu Linh, Zhongyu (Joan) Lu |
| Autore | Linh Nguyen Thi Dieu |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (91 pages) |
| Disciplina | 006.3 |
| Collana | SpringerBriefs in Forensic and Medical Bioinformatics |
| Soggetto topico |
Computational intelligence
Medical informatics Artificial intelligence - Data processing Quantitative research Big data Internet of things Computational Intelligence Health Informatics Data Science Data Analysis and Big Data Big Data Internet of Things Informàtica mèdica Bioinformàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-3029-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. A Value of Data Science in the Medical Informatics: An Overview -- 2. Data science in Medical Informatics: Challenges and Opportunities -- 3. Eminent Role of Machine Learning in the Healthcare Data Management -- 4. Potential and Adoption of Data Science in the Healthcare Analytics -- 5. Emerging Advancement of Data Science in the Healthcare Informatics. |
| Record Nr. | UNINA-9910485591403321 |
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Linh Nguyen Thi Dieu
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Developing Medical Apps and mHealth Interventions [[electronic resource] ] : A Guide for Researchers, Physicians and Informaticians / / by Alan Davies, Julia Mueller
| Developing Medical Apps and mHealth Interventions [[electronic resource] ] : A Guide for Researchers, Physicians and Informaticians / / by Alan Davies, Julia Mueller |
| Autore | Davies Alan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXI, 380 p. 159 illus., 144 illus. in color.) |
| Disciplina | 610.285 |
| Collana | Health Informatics |
| Soggetto topico |
Health informatics
Computer programming Health Informatics Programming Techniques Informàtica mèdica Aplicacions mòbils Salut pública |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-47499-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to mHealth -- Project development methodologies, management and data modelling -- Designing an mHealth intervention -- Application development and testing -- Data collection, storage and security -- Feeding back information to patients and users with visualisations -- Usability testing and deployment -- Designing an mHealth evaluation -- Data analysis methods. |
| Record Nr. | UNINA-9910411944703321 |
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Davies Alan
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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