Prognostic Modeling Using AI' 2022: Prognostic Models in Healthcare: AI and Statistical Approaches |
Submission link | https://easychair.org/conferences/?conf=prognosticmodelingus |
Prognostic Modeling Using AI' 2022
Prognosis research is the study of future outcomes in people with a particular health condition. It provides critical evidence for obtaining, evaluating, and implementing prognostic information within modern healthcare. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. In diagnostic studies, artificial intelligence approaches have been applied to various medical imaging modalities, including radiography, ultrasound, and CT to build more accurate detection/diagnostic models. Machine learning and deep learning can provide techniques and tools that help solve diagnostic and prognostic problems in medical domains. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for the development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. Theoretical insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes, is the utmost need of the healthcare field.
The primary emphasis of this book will be the major technical advancements and research findings in the field of prognostic modelling in Healthcare image and data analysis. The book will concentrate on the recent technologies and studies that have reached the practical level and become available in preclinical and clinical practices in computational intelligence. Areas of interest in this proposed book are the highest quality, original work that will contribute to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in Healthcare image and data analysis.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal, conference or book. The following paper categories are welcome:
Researchers and practitioners are invited to submit their original research work. Full chapters must be submitted by before December 20, 2021 through EasyChair system. The Chapter should not be less than 15 pages and must not exceed 30 pages with respect to Springer format. All submitted chapters will be reviewed on a single-blind review basis. Contributors may also be requested to serve as reviewers for this project.
List of Topics
Suggested topics include (but are not limited to) the following:
- Prognostic Modelling in Healthcare Applications
- Impact of Machine Learning in Healthcare Applications
- Statistics, Machine learning and AI in Prognostics Model Building
- Mathematical models of supervised learning and application to medical diagnosis
- Emerging e-Healthcare problems
- Multimodal and transfer learning in Healthcare application
- Prognostic modelling with the IoT applications
Book Editors
Prof. Tanzila Saba
Dr. Rehman Khan, Senior Researcher, IEEE Senior Member
Dr. Sudipta Roy
Publication
Prognostic Modeling Using AI' 2022 proceedings will be published in the book series “Studies in Big Data” (https://www.springer.com/series/11970) by Springer.
Indexed by SCOPUS, EI Compendex, SCIMAGO and zbMATH.
All books published in the series are submitted for consideration in Web of Science.
Contact
All questions about submissions should be emailed to aidalab@psu.edu.sa