A Context Aware Decision-Making Algorithm for Human-Centric Analytics:Algorithm Development and Use Cases for Health Informatics System
Veena A, Gowrishankar S
Editorial: Bentham Science Publishers
Sinopsis
This reference demonstrates the development of a context aware decision-making health informatics system with the objective to automate the analysis of human centric wellness and assist medical decision-making in healthcare.The book introduces readers to the basics of a clinical decision support system. This is followed by chapters that explain how to analyze healthcare data for anomaly detection and clinical correlations. The next two sections cover machine learning techniques for object detection and a case study for hemorrhage detection. These sections aim to expand the understanding of simple and advanced neural networks in health informatics. The authors also explore how machine learning model choices based on context can assist medical professionals in different scenarios.Key FeaturesReader-friendly format with clear headings, introductions and summaries in each chapterDetailed references for readers who want to conduct further researchExpert contributors providing authoritative knowledge on machine learning techniques and human-centric wellnessPractical applications of data science in healthcare designed to solve problems and enhance patient wellbeingDeep learning use cases for different medical conditions including hemorrhages, gallbladder stones and diabetic retinopathyDemonstrations of fast and efficient CNN models with varying parameters such as Single shot detector, R-CNN, Mask R-CNN, modified contrast enhancement and improved LSTM models.This reference is intended as a primary resource for professionals, researchers, software developers and technicians working in healthcare informatics systems and medical diagnostics. It also serves as a supplementary resource for learners in bioinformatics, biomedical engineering and medical informatics programs and anyone who requires technical knowledge about algorithms in medical decision support systems.ReadershipHealthcare professionals, software developers, engineers, diagnostic technicians, students, academicians and machine learning enthusiasts.
Disponible desde: 16/10/2024.
Longitud de impresión: 140 páginas.
