Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBM.2012.6392623 | BIBM |
Keywords | DocType | Citations |
real data set,Bayesian-based prediction model,BPLT model,proposed BPLT model,real world,personalized medical health care,laboratory test,medical problem,new laboratory test,laboratory test prediction problem,special classification problem,real world application | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohua Hu | 1 | 2819 | 314.15 |
| Joseph Kurian | 2 | 0 | 0.34 |
| Xiangji Huang | 3 | 1551 | 159.34 |
| Jiashu Zhao | 4 | 48 | 6.22 |
| William Melek | 5 | 65 | 3.71 |