Developing AI Platforms for Healthcare at the Enterprise Level: Data Pipelines, Model Management, and Real-Time Clinical Integration
Keywords:
Development of AI platforms, data pipelines, model governance, real-time clinical integration, machine-learning algorithms, Random Forest, predicting health-related conditions, cardiovascular disease, age Vs blood-pressure relationship, high data fidelity, algorithm refinement, regular model updating, clinical decision making, patient outcomes in real-time.Abstract
This study explores the development of AI platforms in healthcare with the focus being on data pipelines, model governance, and real-time clinical integration. Using machine-learning algorithms, in particular, Random Forest, the research examines the performance of AI within the medical domain of predicting health-related conditions, including cardiovascular disease. The results highlight such issues as low accuracy (49%) and instabilities of the model, where there are variations in performance with time. Associations can be made by visual representation as in the age Vs blood-pressure relationship however the small sample makes this inference. The study identifies the highly important role of high data fidelity, algorithm refinement and regular model updating to support clinical decision making and patient outcomes in real-time.
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