Heart rate variability (HRV) as a predictor of anemia in premature infants using Artemis

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Premature infants often receive multiple blood transfusions within the first few weeks of life because of their physiological needs. Anemia is a major contributor to the need for transfusions in premature infants, and current detection practices rely on laboratory testing of blood samples. This thesis introduces a Clinical Decision Support System (CDSS) framework that utilizes high frequency streaming physiological data and laboratory information for clinical insights through visual analytics. The framework leverages the Artemis platform, a Big Data and Artificial Intelligence based CDSS, by exploring relationships between blood transfusions and heart rate variability (HRV). Using Artemis, this thesis aimed to identify patterns in HRV to enable non-invasive detection of physiologically significant anemia through data visualization. This work contributes to health informatics by presenting an integrated CDSS framework and to laboratory sciences by demonstrating the potential of laboratory data integration for non-invasive anemia detection.