- Last Call for Chapter Proposal (1000-2000 Words): 1st October 2020
- Notification of Proposal Approval: 20th November 2020
- Full Chapter Submission: 31st December 2020
- Review of Chapters notification to Authors: 05th February 2021
- Revised Chapter Submission from Chapter Authors: 10th March 2021
- Final Acceptance/Rejection Notifications to Chapter Authors: 31st March 2021
About the Book
This book is being formulated with intent to uncover the stakes and possibilities involved in realising personalised healthcare services through efficient and effective deep learning algorithms. Successful application of deep learning frameworks to enable meaningful, cost-effective personalised health-care services is the primary aim of the healthcare industry in the present scenario.
However, realising this goal requires effective understanding, application and amalgamation of deep-learningand several other computing technologies to deploy such systems in an effective manner. This book shall help clarify understanding of certain key mechanisms and technologies helpful in realising such systems.
It covers topics not limited to: Machine learning: better billing/coding error detection (leading to reduced claims denials); optimization of supply chain for pharmaceuticals; and enhanced data mining, thus guiding effective diagnosis; NLP: Text-to-speech and vice-versa, document and data conversions, patient notes, processing of unstructured data, and query support systems; Deep learning and cognitive computing tools: Enables processing of very large data sets, helps with precise and comprehensive forecast of risks, and delivers recommended actions that improve outcomes for consumers.
It is a novel application domain of deep learning that is of prime importance to human civilization as a whole. It has been predicted as the next big thing in personal health monitoring by the government as well as Forbes.
Call for chapters
- Applications of Deep Learning in Healthcare
- Deep Learning & Clinical Decision Support System
- Deep Learning for Analysis of Electronic Health Records
- Deep Learning for Clinical Decision Support Systems
- Deep Learning in Textural Medical Image Analysis
- Diabetes Detection Using ECG Signals
- Fuzzy Naive Bayesian system for Medical Decision Support
- Improving the Performance of Deep CNNs in Medical Image Segmentation with Limited Resources
- Intelligent Patient Health Record
- Medical Image Detection Using Deep Learning
- NLP for Clinical Data Analysis
- NLP in Medical Science
Dr. Vishal Jain, BVICAM, New Delhi