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DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare

  • R. Sangeetha and Nikola Ivković
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Traditional healthcare systems tend to suffer from issues like disjointed information, late diagnosis, and suboptimal patient activation impact overall therapeutic outcomes and impede timely interventions. The study introduces DigiCure, a simulation-based digitalization framework to optimize the provision of healthcare through the use of technology, as a solution to these issues. The model uses telemedicine, the Internet of medical things (IoMT), artificial intelligence (AI), and electronic health records to design a networked, patient-centered platform. The simulated work used the MIMIC-III dataset, including the diagnostic records, clinical measurements, and patients’ demographics. The simulated IoMT data streams were used to model real-time monitoring conditions, and AI models were trained from the dataset for supporting risk assessment and prediction diagnosis. In addition to that, a prototype for a mobile app was also built to emphasize features like teleconsultation, tracking medications, and booking appointments. From the outcomes of the simulation, performance evaluation reveals that the proposed model, a hybrid architecture combining random forest and Bi-LSTM, outperforms all traditional classifiers across multiple metrics: it achieves accuracy of 92.5%, precision of 91.8%, recall of 91.2%, F1-score of 91.5%, and area under the receiver operating characteristic curve of 94.2%. These results establish a clear margin over CNN-LSTM (accuracy: 90.3%, AUC: 91.7%) and conventional machine learning models such as support vector machine (accuracy: 87.1%) and logistic regression (accuracy: 85.2%). The study holds great promise for live deployment, with the future holding clinical trials and implementation into live hospital systems.

Abstract

Traditional healthcare systems tend to suffer from issues like disjointed information, late diagnosis, and suboptimal patient activation impact overall therapeutic outcomes and impede timely interventions. The study introduces DigiCure, a simulation-based digitalization framework to optimize the provision of healthcare through the use of technology, as a solution to these issues. The model uses telemedicine, the Internet of medical things (IoMT), artificial intelligence (AI), and electronic health records to design a networked, patient-centered platform. The simulated work used the MIMIC-III dataset, including the diagnostic records, clinical measurements, and patients’ demographics. The simulated IoMT data streams were used to model real-time monitoring conditions, and AI models were trained from the dataset for supporting risk assessment and prediction diagnosis. In addition to that, a prototype for a mobile app was also built to emphasize features like teleconsultation, tracking medications, and booking appointments. From the outcomes of the simulation, performance evaluation reveals that the proposed model, a hybrid architecture combining random forest and Bi-LSTM, outperforms all traditional classifiers across multiple metrics: it achieves accuracy of 92.5%, precision of 91.8%, recall of 91.2%, F1-score of 91.5%, and area under the receiver operating characteristic curve of 94.2%. These results establish a clear margin over CNN-LSTM (accuracy: 90.3%, AUC: 91.7%) and conventional machine learning models such as support vector machine (accuracy: 87.1%) and logistic regression (accuracy: 85.2%). The study holds great promise for live deployment, with the future holding clinical trials and implementation into live hospital systems.

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/10.1515/9783111541235-002

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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