A multi-voting enhancement for newborn screening healthcare information system.
The clinical symptoms of metabolic disorders during neonatal period are often not apparent. If not treated early, irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well.
Hsieh
Sung-Huai
SH
Information Systems Office, National Taiwan University Hospital, Taipei, Taiwan.
Cheng
Po-Hsun
PH
Chen
Chi-Huang
CH
Huang
Kuo-Hsuan
KH
Chen
Po-Hao
PH
Weng
Yung-Ching
YC
Hsieh
Sheau-Ling
SL
Lai
Feipei
F
D000465:Algorithms; D020739:Brain Diseases, Metabolic, Inborn / Q000175:diagnosis*; D006801:Humans; D007231:Infant, Newborn; D008764:Methylmalonic Acid / Q000097:blood* / Q000652:urine*; D015997:Neonatal Screening; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D053719:Tandem Mass Spectrometry
D008764:Methylmalonic Acid