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Evolving predictive analytics in healthcare : new AI techniques for real-time interventions /
This book examines machine learning trends in predictive technology to solve real-time healthcare problems. By using real-time data inputs to build predictive models, this new technology can model disease progression, assist with interventions or predict patient outcomes.
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
The Institution of Engineering and Technology,
2022.
|
| Colección: | Healthcare technologies series ;
43. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Evolving predictive analytics in healthcare : |b new AI techniques for real-time interventions / |c edited by Abhishek Kumar, Ashutosh Kumar Dubey, Surbhi Bhatia, Swarn Avinash Kumar, Dac-Nhuong Le. |
| 264 | 1 | |a London : |b The Institution of Engineering and Technology, |c 2022. | |
| 300 | |a 1 online resource : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Healthcare technologies series ; |v 43 | |
| 520 | |a This book examines machine learning trends in predictive technology to solve real-time healthcare problems. By using real-time data inputs to build predictive models, this new technology can model disease progression, assist with interventions or predict patient outcomes. | ||
| 505 | 0 | |a Intro -- Title -- Copyright -- Contents -- About the Editors -- 1 COVID-19 detection in X-ray images using customized CNN model -- 1.1 Introduction -- 1.2 Related work -- 1.2.1 Key contributions and proposed work -- 1.3 Materials and methods -- 1.3.1 Feature extraction and selection -- 1.4 Results and discussion -- 1.5 Conclusion and future scope -- References -- 2 Introducing deep learning in medical diagnosis -- 2.1 Introduction -- 2.2 Literature survey -- 2.3 Overview of DL algorithms -- 2.3.1 Convolutional neural network -- 2.3.2 Recurrent neural network -- 2.3.3 Long short-term memory | |
| 505 | 8 | |a 2.3.4 Restricted Boltzmann machine -- 2.3.5 Deep belief networks -- 2.4 Proposed DL framework for neuro disease diagnosis -- 2.4.1 FAST-RCNN -- 2.4.2 Ten fully connected layer -- 2.5 Preprocessing of dataset -- 2.6 Implementation and results -- 2.7 Conclusion -- References -- 3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML) -- 3.1 Introduction -- 3.1.1 DoS and DDoS attacks -- 3.1.2 Man-in-the-middle (MitM) attack -- 3.1.3 Phishing and spear-phishing attacks -- 3.1.4 Password attack -- 3.1.5 Eavesdropping attack -- 3.1.6 Malware attack | |
| 505 | 8 | |a 3.2 Related work -- 3.3 Cloud computing -- 3.3.1 Machine learning -- 3.3.2 Exploratory data analysis -- 3.4 Results -- References -- 4 Classification methodologies in healthcare -- 4.1 Introduction -- 4.2 Classification algorithms -- 4.2.1 Statistical data -- 4.2.2 Discriminant analysis -- 4.2.3 Decision tree -- 4.2.4 K-nearest neighbor (KNN) -- 4.2.5 Logistic regression (LR) -- 4.2.6 Bayesian classifier -- 4.2.7 Support vector machine (SVM) -- 4.3 Parameter identification -- 4.3.1 Feature selection for classi cation -- 4.4 Real-time applications | |
| 505 | 8 | |a 4.4.1 Classification of patients based on medical record -- 4.4.2 Predictive analytics and diagnostic analytics based on medical records -- 4.4.3 Classification of diseases based on medical imaging -- 4.4.4 Mixed reality-based automation to help aid aging society -- 4.4.5 Tiny ML-based classification systems for medical gadgets -- 4.4.6 Classification systems for insurance claim management -- 4.4.7 Case study: Inspectra from Perceptra -- 4.4.8 Deep learning for beginners -- References -- 5 Introducing deep learning in medical domain -- 5.1 Introduction -- 5.1.1 DL in a nutshell | |
| 505 | 8 | |a 5.1.2 History of DL in the medical field -- 5.1.3 Benefits of DL in the medical domain -- 5.1.4 Challenges and obstacles of DL in the medical domain -- 5.1.5 Opportunities of DL in the medical field -- 5.2 DL applications in the medical domain -- 5.2.1 Drug discovery and medicine precision -- 5.2.2 Detection of diseases -- 5.2.3 Diagnosing patients -- 5.2.4 Healthcare administration -- 5.3 DL for medical image analysis -- 5.3.1 Medical image detection -- 5.3.2 Medical image recognition -- 5.3.3 Medical image segmentation -- 5.3.4 Medical image registration | |
| 500 | |a 5.3.5 Disease diagnosis and quantification | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a Knovel |b ACADEMIC - Software Engineering | ||
| 590 | |a Knovel |b ACADEMIC - Biochemistry, Biology & Biotechnology | ||
| 650 | 0 | |a Artificial intelligence |x Medical applications. | |
| 650 | 0 | |a Predictive analytics. | |
| 650 | 6 | |a Intelligence artificielle en médecine. | |
| 650 | 7 | |a Artificial intelligence |x Medical applications |2 fast | |
| 650 | 7 | |a Predictive analytics |2 fast | |
| 700 | 1 | |a Kumar, Abhishek, |d 1989- |e editor. | |
| 700 | 1 | |a Dubey, Ashutosh Kumar, |e editor. | |
| 700 | 1 | |a Bhatia, Surbhi, |d 1988- |e editor. | |
| 700 | 1 | |a Kumar, Swarn Avinash, |e editor. | |
| 700 | 1 | |a Le, Dac-Nhuong, |d 1983- |e editor. |1 https://isni.org/isni/0000000493180265 | |
| 776 | 0 | 8 | |i Print version: |t Evolving predictive analytics in healthcare. |d Stevenage, Hertfordshire : The Institution of Engineering and Technology, 2022 |z 9781839535116 |w (OCoLC)1338666953 |
| 830 | 0 | |a Healthcare technologies series ; |v 43. | |
| 856 | 4 | 0 | |u https://appknovel.uam.elogim.com/kn/resources/kpEPAHNAI2/toc |z Texto completo |
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