Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework.

Area of Science:

• Health Informatics
• Biomedical Engineering
• Clinical Trial Management

Background:

• Effective monitoring of clinical trials is essential due to the involvement of human subjects and the need for strict protocol adherence.
• Traditional monitoring methods can be resource-intensive and may lack real-time accuracy.
• Information and communication technology offers potential solutions for improving the efficiency and precision of clinical trial oversight.

Purpose of the Study:

• To develop and propose a new conceptual framework for the remote monitoring of clinical trials.
• To leverage machine learning classifiers, specifically Support Vector Machine (SVM) and Artificial Neural Network (ANN), for analyzing physiological data.
• To enhance the decision-making process for clinical trial research teams through accurate participant classification.

Main Methods:

• A prototype framework was designed, comprising data collection, transmission, and analysis/prediction modules.
• Physiological datasets from wearable devices were utilized.
• Bagging Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers were employed after data preprocessing and transformation for training and testing.

Main Results:

• The developed system successfully classified participants into three categories: fit, unfit, and undecided.
• The classification outcomes provide a basis for determining participant eligibility to continue in the trial.
• Experimental analysis demonstrated the efficacy of the proposed machine learning approach.

Conclusions:

• The proposed framework offers a valuable tool for remote clinical trial monitoring.
• The integration of wearable technology and machine learning (SVM and ANN) improves the accuracy and efficiency of participant assessment.
• This approach supports informed and timely decision-making for clinical trial management.