Semantic Web for Effective Healthcare Systems

1 Chapter 1 Table 1.1 Confusion matrix. Table 1.2 Social media reviews for healthcare service (DS1). Table 1.3 Number of reviews of features and hospitals (DS1). Table 1.4 List of top five terms by LDA model. Table 1.5 Sample correlated terms selected by CFS LDA. Table 1.6 List of correlated feature terms selected by CFS LDAmodel. Table 1.7 Performance evaluation.

2 Chapter 2 Table 2.1 Growth of data [15].

3 Chapter 4Table 4.1 List of popular biomedical ontologies.Table 4.2 List of popular biomedical libraries.

4 Chapter 7Table 7.1 The table shows counts of complications that were encountered intra- a...

5 Chapter 9Table 9.1 Different terms used in computational examination [13].Table 9.2 Examples for interoperability standards and ontologies commonly used i...Table 9.3 Resources and applications in human phenotype ontology contexts.

6 Chapter 10Table 10.1 Contingency matrix of positives and negatives.

7 Chapter 11Table 11.1 Patients characteristics in the final dataset (n = 829).Table 11.2 ADL disabilities patients before and after cancer diagnosis.Table 11.3 Q (R, w*), rule for ADL.

8 Chapter 12Table 12.1 List of various diagnostic tools with their webpages [6].

9 Chapter 14Table 14.1 Three-stage model of application of AI for patients in critical care.

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Scrivener Publishing100 Cummings Center, Suite 541J Beverly MA, 01915-6106

Machine Learning in Biomedical Science and Healthcare Informatics

Series Editors: Vishal Jain and Jyotir Moy Chatterjee

In this series, the focus centers on the various applications of machine learning in the biomedical engineering and healthcare fields, with a special emphasis on the most representative learning techniques, namely deep learning-based approaches. Machine learning tasks typically classified into two broad categories depending on whether there is a learning “label” or “feedback” available to a learning system: supervised learning and unsupervised learning. This series also introduces various types of machine learning tasks in the biomedical engineering field from classification (supervised learning) to clustering (unsupervised learning). The objective of the series is to compile all aspects of biomedical science and healthcare informatics, from fundamental principles to current advanced concepts.

Submission to the series: Please send book proposals to drvishaljain83@gmail.com and/or jyotirchatterjee@gmail.com

Publishers at Scrivener Martin Scrivener ( martin@scrivenerpublishing.com) Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

Vishal Jain

Sharda University, India

Jyotir Moy Chatterjee

Lord Buddha Education Foundation, Nepal

Ankita Bansal

Netaji Subhas University of Technology, India

and

Abha Jain

Shaheed Rajguru College of Applied Sciences for Women, Delhi University, India

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA