Background: Configuration of complex Laboratory Developed Tests (LDTs) is a time-consuming and complicated task, potentially leading to inconsistent LDTs in which features constraints remain unresolved and important features could remain unselected.
Objective: Our objective is to address these issues by presenting an automated, health informatics solution which autonomously optimizes feature selection in complex LDTs through Particle Swarm Optimization (PSO). The optimization goal is to minimize inconsistencies and configuration time, and maximize the number of selected features.
Methods: We implemented our technology in a local, secondary-care hospital in Pakistan which configures LDT for a local epidemic disease. First, a list of inconsistent LDT configurations is generated. This is used to initially estimate optimal PSO parameters, which are then used for optimization process.
Results: Results show that PSO is able to minimize 91% inconsistencies between 9 and 11 seconds. The number of selected critical features also increases by 100% in the optimized LDT configuration.
Conclusions: We present a novel and the first application of computational optimization to solve LDT configuration issues.
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| http://dx.doi.org/10.3233/THC-161227 | DOI Listing |
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