Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation
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Sukumar Rajendran
Abstract
To maximize treatment recommendation in online healthcare systems, the study presents CareRecNet, a patient-focused AI framework. CareRecNet is a deep learning-based hybrid architecture that combines long short-term memory (LSTM) networks and a clinical knowledge graph to fuse multimodal patient data, including wearable sensor data, diagnostic test reports, and electronic health records (EHRs), for context-aware decision-making. By aligning treatment regimens with the individual history of every patient, comorbidities, and clinical guidelines, the strategy emphasizes greatly on individualized care. A hospital EHR dataset that was curated and MIMIC-III, two benchmark health datasets, were utilized for a comprehensive study. CareRecNet performed better than traditional models such as traditional LSTM (84.2%) and decision tree-based recommendation systems (79.4%) with 91.6% accuracy in treatment recommendations. Moreover, the framework demonstrated 90.4% recall and 93.1% precision, ensuring reliable and clinically significant recommendations. These results substantiate CareRecNet’s potential for providing precise, personalized treatment pathways and enhancing patient satisfaction and clinical efficacy. The proposed strategy is a major step forward in patient-centric, smart, and adaptable e-healthcare.
Abstract
To maximize treatment recommendation in online healthcare systems, the study presents CareRecNet, a patient-focused AI framework. CareRecNet is a deep learning-based hybrid architecture that combines long short-term memory (LSTM) networks and a clinical knowledge graph to fuse multimodal patient data, including wearable sensor data, diagnostic test reports, and electronic health records (EHRs), for context-aware decision-making. By aligning treatment regimens with the individual history of every patient, comorbidities, and clinical guidelines, the strategy emphasizes greatly on individualized care. A hospital EHR dataset that was curated and MIMIC-III, two benchmark health datasets, were utilized for a comprehensive study. CareRecNet performed better than traditional models such as traditional LSTM (84.2%) and decision tree-based recommendation systems (79.4%) with 91.6% accuracy in treatment recommendations. Moreover, the framework demonstrated 90.4% recall and 93.1% precision, ensuring reliable and clinically significant recommendations. These results substantiate CareRecNet’s potential for providing precise, personalized treatment pathways and enhancing patient satisfaction and clinical efficacy. The proposed strategy is a major step forward in patient-centric, smart, and adaptable e-healthcare.
Chapters in this book
- 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
Chapters in this book
- 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
