"Clinical decision support that integrates multiple elements of laboratory data could be highly useful in enhancing laboratory diagnosis. Using the analyte ferritin in a proof-of-concept, we extracted clinical laboratory data from patient testing and applied a variety of machine learning algorithms to predict ferritin test results using the results from other tests. We show that predicted ferritin results may sometimes better reflect underlying iron status than measured ferritin. We next integrate temporality into predicting multi-variate analytes. We devise an algorithm termed 3D-MICE alternating between cross-sectional imputation and auto-regressive imputation. We show modest performance improvement of the combined algorithm compared to either component alone. We then integrate Gaussian process with mixture model and introduce individualized mixing weights to handle variance in predictive confidence of Gaussian process components. Experiments show that our best model can provide more accurate imputation than the state-of-the-art including 3D-MICE on both synthetic and real-world datasets."
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