Health informatics to optimize complex laboratory developed test configurations.
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.
Federal Urdu University of Arts Science & Technology, Karachi, Pakistan.|National University of Computing and Emerging Sciences, Karachi, Pakistan.
Uzma
U
Afzal
Karachi Institute of Economics and Technology, College of Computing and Information Sciences, Karachi, Pakistan.
Tariq
T
Mahmood
Madni General Hospital, Gujrat City, Pakistan.
Masood
M
Anwar
President Mohammad Ali Jinnah University, Karachi, Pakistan.
Zubair
Z
Shaikh
D000465:Algorithms; D001185:Artificial Intelligence; D035843:Biomedical Research / Q000379:methods*; D006801:Humans; D008490:Medical Informatics; D010154:Pakistan; D010363:Pattern Recognition, Automated
Laboratory developed test; health informatics; inconsistencies; particle swarm optimization