4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies
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Prateek Kumar
Abstract
In order to generate individualized risk scores, predictive analytics is essential for evaluating risk by examining lifestyle decisions, medical histories, and genetic predispositions. It facilitates proactive strategy optimization and the prioritization of high-risk patients. By analyzing proteomic and genetic data, finding susceptibility signs, and forecasting treatment outcomes, machine learning improves personalized medicine. Customized treatment strategies that enhance patient outcomes are the outcome of this. Machine learning in public health makes it possible to analyze health data in real-time for the early identification of disease outbreaks, enabling organizations to effectively allocate resources and respond proactively. Targeted safety precautions are supported by pharmacovigilance, which gives priority to adverse occurrences in the FDA Adverse Event Reporting System (FAERS). By predicting medication efficacy and identifying novel therapeutic targets, machine learning algorithms in drug discovery drastically cut down on development time and expenses. Clinical trial designs are optimized and the rate of discovery is accelerated via automated drug development processes. Notwithstanding these benefits, for wider use in the healthcare industry, problems including data quality, algorithmic bias, and regulatory compliance need to be resolved. Sequential learning and recommender systems are the main topics of this study since they are essential approaches in the ever changing biomedical field.
Abstract
In order to generate individualized risk scores, predictive analytics is essential for evaluating risk by examining lifestyle decisions, medical histories, and genetic predispositions. It facilitates proactive strategy optimization and the prioritization of high-risk patients. By analyzing proteomic and genetic data, finding susceptibility signs, and forecasting treatment outcomes, machine learning improves personalized medicine. Customized treatment strategies that enhance patient outcomes are the outcome of this. Machine learning in public health makes it possible to analyze health data in real-time for the early identification of disease outbreaks, enabling organizations to effectively allocate resources and respond proactively. Targeted safety precautions are supported by pharmacovigilance, which gives priority to adverse occurrences in the FDA Adverse Event Reporting System (FAERS). By predicting medication efficacy and identifying novel therapeutic targets, machine learning algorithms in drug discovery drastically cut down on development time and expenses. Clinical trial designs are optimized and the rate of discovery is accelerated via automated drug development processes. Notwithstanding these benefits, for wider use in the healthcare industry, problems including data quality, algorithmic bias, and regulatory compliance need to be resolved. Sequential learning and recommender systems are the main topics of this study since they are essential approaches in the ever changing biomedical field.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
