Buch, Englisch, Band 64, 311 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 658 g
Reihe: Lecture Notes on Data Engineering and Communications Technologies
Buch, Englisch, Band 64, 311 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 658 g
Reihe: Lecture Notes on Data Engineering and Communications Technologies
ISBN: 978-981-16-0537-6
Verlag: Springer Nature Singapore
This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Technische Wissenschaften Technik Allgemein Modellierung & Simulation
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
Data visualization in the transformation of healthcare industries.- Emerging Healthcare Problems in High Dimensional Data and Dimension Reduction.- Disease Prediction Using Data Mining and Machine Learning Techniques.- Prognostic modeling with the internet of Healthcare things applications.- Cancer Tissue Segmentation in Various Conditions with Semiautomatic and Automatic Approach.- Early Screening of COVID-19 from Chest CT using Deep Learning Technique.- Clinical Decision-Making and Predicting Patient Trajectories.