Machine Learning for Medical Applications : Computational Drug Discovery, Bioimaging, Smart Biomaterials.

Format:

Book

Author/Creator:

Rajamanickam, Ranjith.

Series:

Advanced Mechanical Engineering Series

Advanced Mechanical Engineering Series ; v.14/1

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Physical Description:

1 online resource (712 pages)

Edition:

1st ed.

Place of Publication:

Berlin/Boston : Walter de Gruyter GmbH, 2025.

Summary:

Machine Learning for Medical Applications - Volume I provides an in-depth look into the frontier of artificial intelligence in healthcare, bringing together contributions from leading researchers and innovators.

Contents:

Intro

Contents

List of contributors

Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity

1 Introduction

2 EHR management systems

2.1 Fundamentals of blockchain-based EHR systems

2.2 EHR

2.3 Overview of traditional EHR management systems

2.4 Evolution of blockchain-based EHR solutions

2.5 Key milestones in the evolution of blockchain-based EHR solutions

2.6 Key components and architecture of blockchain-based EHR systems

3 Security and privacy in blockchain-based EHR systems

3.1 Importance of security and privacy in healthcare data

3.2 Security challenges in traditional EHR systems

3.3 Enhancement of blockchain security in EHR management

3.4 Privacy-preserving techniques in blockchain-based EHRs

3.5 Compliance with healthcare regulations (e.g., GDPR, HIPAA)

4 Interoperability and data sharing in blockchain-based EHR systems

4.1 Interoperability challenges in healthcare data exchange

4.2 Role of blockchain in enabling interoperability

4.3 Standards and protocols for interoperable EHR systems

4.4 Blockchain-based solutions for seamless data sharing

4.5 Case studies of successful interoperable EHR implementations

5 Smart contracts for access control and governance

5.1 Introduction to smart contracts in blockchain technology

5.2 Application of smart contracts in healthcare

5.3 Role-based access control (RBAC) in EHR management

5.4 Implementing fine-grained access control with smart contracts

5.5 Governance models for blockchain-based EHR systems

6 Data integrity and auditability in blockchain-based EHR systems

6.1 Importance of data integrity in healthcare data management

6.2 Challenges in ensuring data integrity in traditional EHRs

6.3 Ensuring blockchain data integrity and immutability.

6.4 Auditing and traceability features of blockchain-based EHR systems

6.5 Real-world examples of data integrity assurance in blockchain EHRs

7 Conclusion

References

AI-powered sensors and devices for sustained health tracking

2 Overview of biomedical sensors and devices

2.1 Types of biomedical sensors and devices

2.2 Implantable sensors

2.3 Remote monitoring systems

2.4 Diagnostic and imaging sensors

2.5 Therapeutic devices

3 Functionality and applications

3.1 Vital signs monitoring

3.2 Chronic disease management

3.3 Preventive and predictive health

3.4 Data acquisition and analysis

3.5 User interaction and feedback

4 Technological advancements

4.1 Miniaturization and wearability

4.2 Connectivity and communication

4.3 Materials and manufacturing

4.4 Enhanced sensor capabilities

4.5 Regulatory and compliance advances

5 Challenges and limitations

5.1 Accuracy and reliability

5.2 Data privacy and security

5.3 User acceptance and compliance

5.4 Cost and accessibility

5.5 Technical and operational issues

6 Future directions

6.1 Emerging technologies

6.2 Personalized and precision health

6.3 Interdisciplinary research and collaboration

6.4 Global health and accessibility

6.5 Regulatory and ethical considerations

Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring

2 Overview of biomedical sensors

2.1 Definition and purpose of biomedical sensors

2.2 Historical development and milestones

2.3 Types of biomedical sensors

2.3.1 Electrochemical sensors

2.3.2 Optical sensors

2.3.3 Temperature sensors

2.3.4 Pressure sensors

2.3.5 Bioelectrical sensors

2.4 Key components and technologies

2.4.1 Sensing elements.

2.4.2 Signal transduction mechanisms

2.4.3 Power supply and energy management

2.4.4 Data processing and communication

2.4.5 Interface and user interaction

2.5 Importance in modern healthcare

3 Evolution and advancements in sensor technology

3.1 Early biomedical sensors: limitations and challenges

3.2 Advances in materials science

3.3 Miniaturization and nanotechnology

3.4 Wireless and wearable sensor technologies

3.5 Integration with mobile health (mHealth) applications

4 Importance of AI in continuous health monitoring

4.1 Role of AI in data processing and analysis

4.2 Enhancing accuracy and precision of sensors

4.3 Real-time health monitoring and alerts

4.4 Predictive analytics for early disease detection

4.5 Personalization of health monitoring and treatment

5 Key AI technologies used in biomedical sensors

5.1 Machine learning algorithms

5.2 Deep learning and neural networks

5.3 Signal processing techniques

5.3.1 Noise reduction and filtering

5.3.2 Signal denoising techniques

5.3.3 Feature extraction and selection

5.3.4 Data fusion and multisensory integration

5.3.5 Real-time signal processing

5.4 Data fusion and multisensor integration

5.5 Edge computing and AI at the sensor level

6 Case studies and applications

6.1 AI-enhanced wearable health monitors

6.2 Implantable sensors for chronic disease management

6.3 Remote patient monitoring systems

6.4 AI in telehealth and telemedicine

6.5 Success stories and clinical trials

Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring

2 Introduction to biomedical sensors

2.1 Definition and classification of biomedical sensors

2.2 Historical evolution of biomedical sensors.

2.3 Key functionalities of biomedical sensors in health monitoring

2.3.1 Real-time data acquisition and monitoring of data

2.3.2 Wearable and implantable device compatibility

2.3.3 Transmission of data and material and person-to-person interaction

2.3.4 Personalization and adaptation

2.3.5 Early warning system and predictive modeling

2.3.6 Navigation and alert interfaces

2.4 Role of sensors in preventive healthcare

2.5 Current market trends and innovations in biomedical sensors

2.5.1 Emergence of wearable sensors

2.5.2 Combination of AI and ML

2.5.3 Multimodal and hybrid sensors, and their development

2.5.4 New techniques and technologies of noncontact and telemetric recording

3 Types of biomedical sensors

3.1 Wearable sensors for continuous monitoring

3.2 Implantable sensors for internal monitoring

3.3 Noninvasive sensors

3.3.1 Optical sensors

3.3.2 Electrochemical sensors

3.3.3 Acoustic sensors

3.3.4 Bioimpedance sensors

3.3.5 Thermal sensors

3.4 Biosensors

3.5 Optical, chemical, and mechanical sensors

4 Technological advancements in sensor design

4.1 Flexible and stretchable electronics in biomedical sensors

4.1.1 Flexible and stretchable electronics: An overview

4.1.2 Advantages of flexible electronics in biomedical application of sensors

4.1.3 Uses of flexible electronics for biomedical sensors

4.1.4 The future of challenges

4.2 Nanotechnology-enhanced sensors for high sensitivity

4.2.1 Quantum dots in biosensing

4.2.2 Carbon nanotube and graphene in sensors

4.2.3 Nano coatings for high performance

4.2.4 Afflation of combining nanotechnology with microfluidics

4.2.5 Novelty in nanotechnology: nanostructured sensing platforms

4.3 Microelectromechanical systems (MEMS) in biomedical applications

4.3.1 Overview of MEMS technology.

4.3.2 MEMS sensors used in physiological monitoring

4.4 Advances in wireless and battery-free sensor technologies

4.4.1 Wireless communication technologies

4.4.2 Battery-less sensor technologies

4.5 Hybrid and multimodal sensor systems for comprehensive monitoring

4.5.1 Multiplexed multiple sensing

4.5.2 Patient compliance and comfort can be easily enhanced

4.5.3 Data fusion by applying ensemble learning and data mining

4.5.4 Some applications in personalized and preventive medicine

5 Biomedical sensors in chronic disease management

5.1 Sensors for cardiovascular health monitoring

5.1.1 ECG sensors

5.1.2 Blood pressure sensors

5.1.3 Pulse oximeters

5.1.4 Heart rate variability (HRV) sensors

5.2 Glucose monitoring sensors for diabetes management

5.2.1 General information on glucose monitoring devices

5.2.2 The expectations to improvements in glucose sensors via technology

5.2.3 Compatibility with insulin delivery systems

5.2.4 Issues and innovations

5.3 Respiratory monitoring sensors for pulmonary diseases

5.3.1 Uses in managing diseases

5.3.2 Challenges and considerations

5.4 Neurological monitoring

5.4.1 EEG sensors

5.4.2 ICP monitors

5.4.3 Brain-computer interface (BCI)

5.4.4 Portable electrical stimulation apparatus

5.4.5 Intelligent prostheses and adaptive neuroprosthetics

5.5 Long-term monitoring solutions for geriatric care

5.5.1 Wearable health monitoring devices

5.5.2 Remote health monitoring systems

5.5.3 Wearable sensors for internal recording

5.5.4 Integrated health monitoring systems

5.5.5 Fall-risk identification and mitigation systems

5.5.6 Challenges and considerations

6 Challenges in biomedical sensor development

6.1 Sensor accuracy and calibration issues

6.1.1 Role of precision on biomedical sensors.

6.1.2 Calibration procedures.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

3-11-150320-8

OCLC:

1553053075