A Scalable Data Engineering Approach to AI-Powered Health Risk Prognosis
Keywords:
Predictive analysis, healthcare, multi-cloud computing, distributed data engineering, privacy-preserving artificial intelligence.Abstract
Predictive healthcare analytics aims to anticipate future clinical events in the form of predictions from individual patients or group cohorts by devising and applying sophisticated analytical models to relevant data sets. The supporting data engineering enables the acquisition of ready-to-use, progress-ready data that meet the quality, temporal, and licensing requirements of analytics and predictive models. Multi-cloud data engineering tools and services play a crucial role in this context and can be fully exploited to develop a cloud-agnostic solution for the central data engineering life cycle. Such an approach helps to ingest, organize, and harmonize data from multiple sources and supports both the descriptive and the predictive phases of healthcare analytics.
The use of Multi-Cloud environments, defined as cloud solutions that combine services from multiple providers belonging to different service models and technology stacks, reduces the risk of vendor lock-in while providing access to the best data engineering cloud capability for a specific use case, promoting the idea of Cloud for Data Engineering. The subsequently applied model development and deployment — typically concentrated in dedicated environments within the cloud — focus on predictive healthcare analytics, which aims to anticipate clinical conditions. Risk stratification models derived from these analytical methodologies are intended to provide clinicians and the healthcare organization with useful information for subsequent decision-making and care activities.
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