Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications
-
B. Yamini
Abstract
Due to the modern developments in genomic studiesgenomic studies and technologies in computing, the concept of a new form of treatment called the personalized medicine has emerged, which can be translated as one’s unique treatment plan considering genetic makeup. This chapter of the book focuses on the principles of profile-specificprofile-specific pharmacotherapy and the modern technologies it is based on to describe the powerful potential of personalized therapy in preventive healthcare and early diagnosis. Integrating machine learning techniques with the field of bioinformatics simplifies the analysis of what used to be complex biological data, which aids in the recognition of such genetic biomarkers and the development of smart medication delivery systems. Nonetheless, there are challenges such as access to resources inequality, very expensive, and moral issues; these need to be addressed to ensure equal use within different groups. This chapter also examines how personalized medicine complements conventional care in order to clear barriers and enhance opportunities for tailored care. It speaks strongly for intercultural genetic research and context-sensitive teaching. This chapter aims to help the readers gain a clearer understanding of how the concept of personalized medicine can revolutionize healthcare systems and improve the quality of patient lives worldwide, through analysis.
Abstract
Due to the modern developments in genomic studiesgenomic studies and technologies in computing, the concept of a new form of treatment called the personalized medicine has emerged, which can be translated as one’s unique treatment plan considering genetic makeup. This chapter of the book focuses on the principles of profile-specificprofile-specific pharmacotherapy and the modern technologies it is based on to describe the powerful potential of personalized therapy in preventive healthcare and early diagnosis. Integrating machine learning techniques with the field of bioinformatics simplifies the analysis of what used to be complex biological data, which aids in the recognition of such genetic biomarkers and the development of smart medication delivery systems. Nonetheless, there are challenges such as access to resources inequality, very expensive, and moral issues; these need to be addressed to ensure equal use within different groups. This chapter also examines how personalized medicine complements conventional care in order to clear barriers and enhance opportunities for tailored care. It speaks strongly for intercultural genetic research and context-sensitive teaching. This chapter aims to help the readers gain a clearer understanding of how the concept of personalized medicine can revolutionize healthcare systems and improve the quality of patient lives worldwide, through analysis.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
