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Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems

  • Shakila Basheer
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Digitalization of healthcare has the potential to revolutionize patient care completely, but as a result of disjointed processing of data and a lack of awareness of context, current systems frequently are not able to provide timely, personalized recommendations. Poor patient risk stratification and inefficient treatment flows are the outcomes of the fact that traditional machine learning algorithms fail to deal with sparse, heterogeneous, and relational nature of healthcare data. We introduce PATHTRACK-RL, a new deep learning approach that uses graph attention networks to learn intricate interrelations between clinical procedures, diagnostic histories, and patient data, to overcome these limitations. To allow efficient feature extraction from high-dimensional multimodal data, such as structured electronic health records, lab results, and physician notes, a stacked variational autoencoder is applied. In addition, a policy network based on reinforcement learning predicts long-term outcomes through a reward-guided decision engine to facilitate real-time optimization of treatment sequence. In comparison to XGBoost, feedforward neural networks, and Transformer models, PATHTRACK-RL offers 94.1% prediction accuracy for early risk detection, 91.8% precision for treatment suggestions, and an average 11.2% improvement in patient outcome metrics (i.e., reduced ICU readmissions), measured by testing on the MIMIC-IV and eICU Collaborative Research datasets. PATHTRACK-RL provides a robust and explainable method for next-generation, patient-oriented clinical decision support systems by combining relational graph modeling with optimization through reinforcement.

Abstract

Digitalization of healthcare has the potential to revolutionize patient care completely, but as a result of disjointed processing of data and a lack of awareness of context, current systems frequently are not able to provide timely, personalized recommendations. Poor patient risk stratification and inefficient treatment flows are the outcomes of the fact that traditional machine learning algorithms fail to deal with sparse, heterogeneous, and relational nature of healthcare data. We introduce PATHTRACK-RL, a new deep learning approach that uses graph attention networks to learn intricate interrelations between clinical procedures, diagnostic histories, and patient data, to overcome these limitations. To allow efficient feature extraction from high-dimensional multimodal data, such as structured electronic health records, lab results, and physician notes, a stacked variational autoencoder is applied. In addition, a policy network based on reinforcement learning predicts long-term outcomes through a reward-guided decision engine to facilitate real-time optimization of treatment sequence. In comparison to XGBoost, feedforward neural networks, and Transformer models, PATHTRACK-RL offers 94.1% prediction accuracy for early risk detection, 91.8% precision for treatment suggestions, and an average 11.2% improvement in patient outcome metrics (i.e., reduced ICU readmissions), measured by testing on the MIMIC-IV and eICU Collaborative Research datasets. PATHTRACK-RL provides a robust and explainable method for next-generation, patient-oriented clinical decision support systems by combining relational graph modeling with optimization through reinforcement.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-013

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
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