Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence
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Pampana Murali
Abstract
Unprecedented volumes of multimodal data have been introduced by the quick digitization of healthcare systems, necessitating real-time analytics for precise diagnosis and treatment planning. However, the fragmented data integration and lack of adaptive learning capabilities of current clinical decision support systems (CDSS) frequently lead to unsatisfactory performance, which causes results to be erroneous or delayed. In order to overcome this significant limitation, this research suggests the reinforced multi-objective optimization framework for healthcare decision intelligence (RMO-HDI), which combines Pareto-based optimization and deep reinforcement learning to dynamically customize patient-specific diagnostic routes. Through the use of a hybrid model that combines NSGA-II and proximal policy optimization (PPO), the system is able to provide treatment workflows with precise, adaptive suggestions. In comparison to traditional rule-based and static machine learning approaches, experimental results on benchmark health data such as MIMIC-IV and PhysioNet show that the proposed RMO-HDI approach improves treatment recommendation accuracy by 21.9%, decreases the rate of false positives by 23.4%, and increases diagnostic accuracy by 18.6%. The findings reinforce how RMO-HDI can facilitate smarter, real-time, and patient-specific decision-making frameworks, thereby revolutionizing healthcare transformation.
Abstract
Unprecedented volumes of multimodal data have been introduced by the quick digitization of healthcare systems, necessitating real-time analytics for precise diagnosis and treatment planning. However, the fragmented data integration and lack of adaptive learning capabilities of current clinical decision support systems (CDSS) frequently lead to unsatisfactory performance, which causes results to be erroneous or delayed. In order to overcome this significant limitation, this research suggests the reinforced multi-objective optimization framework for healthcare decision intelligence (RMO-HDI), which combines Pareto-based optimization and deep reinforcement learning to dynamically customize patient-specific diagnostic routes. Through the use of a hybrid model that combines NSGA-II and proximal policy optimization (PPO), the system is able to provide treatment workflows with precise, adaptive suggestions. In comparison to traditional rule-based and static machine learning approaches, experimental results on benchmark health data such as MIMIC-IV and PhysioNet show that the proposed RMO-HDI approach improves treatment recommendation accuracy by 21.9%, decreases the rate of false positives by 23.4%, and increases diagnostic accuracy by 18.6%. The findings reinforce how RMO-HDI can facilitate smarter, real-time, and patient-specific decision-making frameworks, thereby revolutionizing healthcare transformation.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
