Artificial Intelligence for Healthcare Applications and Management

Erscheinungsjahr 2022
ISBN: 978-0-12-824521-7
Verlag: Elsevier Inc

Buch, Englisch, 548 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1130 g

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.

Zielgruppe

<p>Researchers, professionals, and graduate students in computer science and engineering, bioinformatics, medical informatics, and biomedical and clinical engineering.</p>

Autoren/Hrsg.

Goldberg, Saveli

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik

Inhaltsverzeichnis

1. Introduction
Boris Galitsky
2. Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis
Boris Galitsky
3. Obtaining supported decision trees from text for health system applications
Boris Galitsky
4. Search and prevention of errors in medical databases
Saveli Goldberg
5. Overcoming AI applications challenges in health: Decision system DINAR2
Saveli Goldberg and Mark Prutkin
6. Formulating critical questions to the user in the course of decision–making
Boris Galitsky
7. Relying on discourse analysis to answer complex questions by neural machine reading comprehension
Boris Galitsky
8. Machine reading between the lines (RBL) of medical complaints
Boris Galitsky
9. Discourse means for maintaining a proper rhetorical flow
Boris Galitsky
10. Dialogue management based on forcing a user through a discourse tree of a text
Boris Galitsky
11. Building medical ontologies relying on communicative discourse trees
Boris Galitsky and Dmitry Ilvovsky
12. Explanation in medical decision support systems
Saveli Goldberg
13. Passive decision support for patient management
Saveli Goldberg and Stanislav Belyaev
14. Multimodal discourse trees for health management and security
Boris Galitsky
15. Improving open domain content generation by text mining and alignment
Boris Galitsky

Über Autor(innen)

Goldberg, Saveli
Dr. Saveli Goldberg has contributed biostatistics and machine learning technologies to research at Harvard Medical School and Massachusetts General Hospital for the last 20 years, where he is currently a biostatistician and data analyst. The author of more than 80 publications and 2 patents, he is currently researching several projects in the field of radiation oncology and endocrinology. The main areas of his research include (a) optimal strategies in cancer radiation therapy, (b) optimal targets and strategies in the treatment of diabetes and hypertension, (c) the optimal combination of expert and artificial intelligence to get the right solution, (d) explanation of the machine learning solution, and (e) the relationship of electronic documentation to patient outcomes.