A Bayesian-based prediction model for personalized medical health care

In this paper, we present a Bayesian-based Personalized Laboratory Tests prediction (BPLT) model to solve a real world medical problem: how to recommend laboratory tests to a group of patients? Given a patient who has conducted several laboratory tests, BPLT model recommends further laboratory tests that are the most related to this patient. We regard this laboratory test prediction problem as a special classification problem, where a new laboratory test belongs to either a "taken" or "not-taken" class. Our goal is to find the laboratory tests with high probability of "taken" and low probability of "not taken". Based on Bayesian method, the BPLT model builds a weighting function to investigate the correlations among laboratory tests and generate the rank of laboratory tests. In order to evaluate the proposed BPLT model, we further propose a novel evaluation metric to subjectively measure the accuracy of BPLT model. Experimental results show that BPLT model achieves good performance on the real data sets and provides a good solution to our real world application.