Advancing Healthcare Through Decision Intelligence : Machine Learning, Robotics, and Analytics in Biomedical Informatics / edited by Somen Dey [and four others].

Format:

Book

Contributor:

Somen Dey, editor.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (323 pages)

Edition:

First edition.

Place of Publication:

London, England : Elsevier Inc., [2025]

Summary:

Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics demonstrates real-world applications of decision intelligence - specifically machine learning, robotics, and analytics - to drive innovation and improvements in healthcare delivery and outcomes.

Contents:

Front Cover

Advancing Healthcare through Decision Intelligence

Copyright Page

Contents

List of contributors

1 Foundations of decision intelligence in healthcare: Integrating machine learning, robotics, and biomedical analytics

1.1 Introduction to the edited volume

References

Theme 1 Biomedical informatics: data analytics for improved healthcare outcomes

2 Deep learning techniques for agricultural plant health assessment: a case study on tomato plant

2.1 Introduction

2.2 Methodology

2.2.1 Dataset preparation

2.2.2 Data augmentation

2.2.3 Deep learning models

2.2.3.1 VGG16

2.2.3.2 ResNet50

2.2.3.3 InceptionV3

2.2.3.4 MobileNetV2

2.2.4 Loss function and optimiser

2.2.5 Model training

2.2.6 Evaluation metrics

2.3 Results and discussion

2.4 Conclusions

3 A systematic analysis of effectiveness of music therapy in children with autism spectrum disorder

3.1 Introduction

3.1.1 Literature survey

3.2 Methodology

3.2.1 Research design

3.2.2 Participant delection

3.2.3 Randomization and control

3.2.4 Intervention

3.2.5 Outcome measures

3.2.6 Data collection and analysis

3.2.7 Ethical considerations

3.2.8 Limitations and future directions

3.2.9 Architecture

3.2.10 Pretraining

3.2.11 Input and preprocessing

3.2.12 Feature extraction

3.2.13 Fully connected layers

3.2.14 Training

3.2.15 Face recognition

3.2.16 Quiz version

3.2.17 Webcam version

3.3 Results

3.4 Discussion

Acknowledgment

4 Bioinformatics, healthcare informatics and analytics: an imperative for improved healthcare system

4.1 Introduction

4.1.1 Importance of bioinformatics in healthcare

4.1.1.1 Bioinformatics' significance in medication development and repurposing

4.1.1.1.1 Drug development

4.1.1.1.2 Drug repurposing.

4.1.1.1.3 The importance of bioinformatics for precision medicine

4.1.1.1.4 Bioinformatics's significance in daily life

4.1.2 Role of healthcare informatics in improving patient care

4.1.2.1 Health informatics enhances patient care in five ways

4.2 Bioinformatics in healthcare

4.2.1 DNA sequencing and analysis

4.2.1.1 Excelra: enabling next-generation sequencing modalities

4.2.1.2 Molecular biology, genomics, and DNA sequencing: the three-pronged indicator of scientific breakthrough

4.2.1.3 Examining the potential of DNA sequence analysis in drug development

4.2.2 Drug development &amp

pharmacogenomics

4.2.2.1 Drug target identification

4.2.2.2 Bioinformatics and pharmacogenomics' roles in the process of finding and developing new drugs

4.2.2.3 Healthcare informatics

4.2.3 Predictive analytics for disease prevention

4.2.3.1 Use of predictive analytics in healthcare

4.2.3.2 Benefits of predictive analytics in healthcare: (Fig. 4.4)

4.2.4 Challenges in bioinformatics: data integration, scalability, and standardization

4.2.4.1 Data integration

4.2.4.2 Challenges and opportunities

4.2.4.3 Integration of healthcare informatics systems

4.2.4.4 Future directions

4.2.4.5 Where is bioinformatics headed?

4.2.4.6 Key player in the future of bioinformatics

4.2.5 Artificial intelligence and machine learning in healthcare

4.2.5.1 Clinical risk prediction models that are self-monitoring, dynamic, and automatically updated

4.2.5.2 Predictive modeling and the learning health system's vision

4.2.5.3 Frameworks for clinical predictive algorithms validation

4.2.6 Blockchain technology for secure medical data management

4.2.6.1 Data integrity

4.2.6.2 Security

4.2.6.3 Decentralization

4.2.6.4 Interoperability

4.2.6.5 Transparency and auditability.

4.2.6.6 Research and analytics

4.2.6.7 Supply chain management

4.3 Conclusion

5 Measuring the impact of predictive analytics on patient satisfaction

5.1 Introduction

5.2 Literature review

5.3 Methodology

5.3.1 Study design

5.3.2 Data collection

5.3.2.1 Patient demographics and historical data

5.3.2.2 Real-time patient feedback

5.3.2.3 Predictive analytics data

5.3.2.4 External factors

5.3.3 Predictive analytics models

5.3.3.1 Machine learning models

5.3.3.2 Deep learning models

5.3.3.3 Real-time analytics

5.3.3.4 Model evaluation and refinement

5.3.4 Ethical considerations

5.3.5 Statistical

5.3.6 Reporting and feedback loop

5.4 Result analysis

5.5 Conclusion

Theme 2 Decision intelligence in biomedical data analytics and management

6 Hybrid optimization of bag composition for disease diagnosis: integrating teaching-learning-based optimization with genetic algorithm

6.1 Introduction

6.1.1 Background

6.1.2 Motivation

6.2 Proposed methodology

6.2.1 Novel fitness function

6.3 Experimental setup and results analysis

6.3.1 Experimental results

6.3.2 Analysis of results

6.3.3 Statistical analysis

6.4 Conclusion

6.4.1 Future Directions

Theme 3 Robotics and robots in biomedical informatics

7 Blockchain in healthcare: Unveiling trends and applications through bibliometric and thematic analysis

7.1 Introduction

7.2 Methodology

7.3 Results

7.3.1 Annual scientific production

7.3.2 Global cited documents

7.3.3 Trend topics

7.3.4 Most influential sources

7.3.5 Word cloud analysis

7.3.6 Thematic analysis

7.4 Conclusion

8 Enhancing social skills development in children with autism through robotic interventions

8.1 Introduction.

8.2 Traditional therapies and their limitations

8.3 Types of robotic interventions

8.4 Social engagement in human-robot interaction

8.5 Practical considerations of robotic interventions

8.6 Considerations and challenges

8.7 Conclusion and future directions

9 Intelligent ankle-foot prosthetics: from engineering fundamentals to integrated artificial intelligence systems

9.1 Introduction

9.2 Engineering fundamentals of ankle-foot prosthetics

9.2.1 Material selection

9.2.2 Gait biomechanics

9.3 Prosthetic foot

9.3.1 Passive ankle-foot

9.3.2 Powered ankle-foot

9.3.3 Sensor-based data acquisition and artificial intelligence

9.3.4 Integration of artificial intelligence and control systems

9.4 Summary

Theme 4 Precision medicine and personalized treatment

10 Precision medicine and personalized treatment

10.1 Introduction

10.2 Related work

10.3 Foundations of precision medicine

10.3.1 Genomics and molecular biology

10.3.2 Advances in Omics technologies

10.3.3 Biomarkers and their significance

10.4 Role of data in precision medicine

10.4.1 Data analytics and artificial intelligence in precision medicine

10.4.2 Integration of big data in healthcare analytics

10.5 Transformative impact on patient outcomes and healthcare economics

10.5.1 Tailored treatment regimens and individualized care

10.6 Interoperability and standardization

10.6.1 Interoperability

10.6.2 Standardization

10.7 Case studies

10.7.1 Global Initiatives and collaborations

10.7.2 Precision medicine initiatives worldwide

10.7.3 Collaborative efforts and consortia

10.7.4 ELSI implications

10.8 Conclusion

11 Voxel-based analysis and advanced techniques for MRI scan classification in early diagnosis of Parkinson's disease

11.1 Introduction.

11.2 Methodology

11.2.1 Data collection

11.2.2 Voxel-based morphometry

11.2.3 Data augmentation

11.2.4 Detection of region of interest

11.2.5 Feature extraction from region of interest

11.2.6 Feature selection

11.2.6.1 Principal component analysis

11.2.7 Classification algorithm

11.2.7.1 Support vector machine

11.2.7.2 K-nearest neighbours

11.2.7.3 Random Forest

11.2.7.4 Navie Bayes

11.2.7.5 Multi-layer perceptron

11.2.7.6 Logistic regression

11.3 Result

11.4 Conclusion

Acknowledgement

12 Predicting metallic implant degradation through patient-specific computational modeling

12.1 Introduction

12.2 Popular methods for degradation prediction of metallic implants

12.2.1 Finite element analysis

12.2.2 Molecular dynamics

12.2.3 Machine learning driven modeling

12.3 Conclusion

Theme 5 Ethics, trust, and explainability in biomedical analytics

13 Ethical concerns in healthcare analytics: exploring the complexities of data-driven decision making

13.1 Introduction

13.2 Ethical considerations in healthcare analytics

13.2.1 Importance of ethics

13.2.2 Informed consent in data collection and use

13.2.3 Bias and fairness

13.3 Discrimination in healthcare analytics

13.4 Privacy concerns in healthcare analytics

13.4.1 Data protection regulations

13.4.2 Anonymization and deidentification

13.5 Security in healthcare analytics

13.5.1 Cybersecurity threats

13.5.2 Security measures

13.5.3 Best practices

13.6 Explainability and Interpretability in healthcare analytics

13.6.1 Challenges

13.6.1.1 Complexity of models

13.6.1.2 Trade-off between accuracy and interpretability

13.6.1.3 Data quality and bias

13.6.1.4 Model validation and performance

13.6.1.5 Regulatory and legal considerations.

13.6.1.6 User education and acceptance.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9780443264818

0443264813

OCLC:

1513421482