Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction.
AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.
1. Chapter 1 : Introduction - p. Page vii 2. Chapter 2 : Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis - p. Page 9 3. Chapter 3 : Obtaining supported decision trees from text for health system applications - p. Page 66 4. Chapter 4 : Search and prevention of errors in medical databases - p. Page 108 5. Chapter 5 : Overcoming AI applications challenges in health: Decision system DINAR2 - p. Page 131 6. Chapter 6 : Formulating critical questions to the user in the course of decision-making - p. Page 158 7. Chapter 7 : Relying on discourse analysis to answer complex questions by neural machine reading comprehension - p. Page 197 8. Chapter 8 : Machine reading between the lines (RBL) of medical complaints - p. Page 230 9. Chapter 9 : Discourse means for maintaining a proper rhetorical flow - p. Page 274 10. Chapter 10 : Dialogue management based on forcing a user through a discourse tree of a text - p. Page 316 11. Chapter 11 : Building medical ontologies relying on communicative discourse trees - p. Page 357 12. Chapter 12 : Explanation in medical decision support systems - p. Page 410 13. Chapter 13 : Passive decision support for patient management - p. Page 428 14. Chapter 14 : Multimodal discourse trees for health management and security - p. Page 446 15. Chapter 15 : Improving open domain content generation by text mining and alignment - p. Page 485 16. Index - p. Page 519
Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction.
AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.
1. Chapter 1 : Introduction - p. Page vii 2. Chapter 2 : Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis - p. Page 9 3. Chapter 3 : Obtaining supported decision trees from text for health system applications - p. Page 66 4. Chapter 4 : Search and prevention of errors in medical databases - p. Page 108 5. Chapter 5 : Overcoming AI applications challenges in health: Decision system DINAR2 - p. Page 131 6. Chapter 6 : Formulating critical questions to the user in the course of decision-making - p. Page 158 7. Chapter 7 : Relying on discourse analysis to answer complex questions by neural machine reading comprehension - p. Page 197 8. Chapter 8 : Machine reading between the lines (RBL) of medical complaints - p. Page 230 9. Chapter 9 : Discourse means for maintaining a proper rhetorical flow - p. Page 274 10. Chapter 10 : Dialogue management based on forcing a user through a discourse tree of a text - p. Page 316 11. Chapter 11 : Building medical ontologies relying on communicative discourse trees - p. Page 357 12. Chapter 12 : Explanation in medical decision support systems - p. Page 410 13. Chapter 13 : Passive decision support for patient management - p. Page 428 14. Chapter 14 : Multimodal discourse trees for health management and security - p. Page 446 15. Chapter 15 : Improving open domain content generation by text mining and alignment - p. Page 485 16. Index - p. Page 519
