Artificial Intelligence and Machine Learning in Health Care and Medical Sciences : Best Practices and Pitfalls / edited by Gyorgy J. Simon, Constantin Aliferis.

Format:

Book

Contributor:

Simon, Gyorgy J., Editor.

Aliferis, Constantin., Editor.

Series:

Health Informatics, 2197-3741

Language:

English

Subjects (All):

Medical informatics.

Medical care.

Bioinformatics.

Public health.

Health Informatics.

Health Care.

Public Health.

Local Subjects:

Health Informatics.

Health Care.

Bioinformatics.

Public Health.

Physical Description:

1 online resource (XXVI, 810 p. 146 illus., 130 illus. in color.)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2024.

Summary:

This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.

Contents:

Predictive Analytics

Machine Learning

Artificial Intelligence

Data Mining

Clinical Risk Models

Clinical Risk Stratification

Data Science

Causal Discovery

Causal Inference

Causal Discovery in Health Sciences

Causal Inference In Health Sciences

Ehr Data Analytics

Medical Knowledge Discovery

Biomedical Machine Learning

Biomedical Artificial Intelligence

Healthcare Machine Learning

Healthcare Artificial Intelligence

Translational Science Machine Learning

Machine Learning for Biological Discovery

Machine Learning in Bioinformatics

Machine Learning in Genomics.

ISBN:

9783031393556

3031393554