title: A multi-voting enhancement for newborn screening healthcare information system.
abstract: 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.
authors: Information Systems Office, National Taiwan University Hospital, Taipei, Taiwan.
authors: Sung-Huai
authors: SH
authors: Hsieh
authors: Po-Hsun
authors: PH
authors: Cheng
authors: Chi-Huang
authors: CH
authors: Chen
authors: Kuo-Hsuan
authors: KH
authors: Huang
authors: Po-Hao
authors: PH
authors: Chen
authors: Yung-Ching
authors: YC
authors: Weng
authors: Sheau-Ling
authors: SL
authors: Hsieh
authors: Feipei
authors: F
authors: Lai
mesh_terms: 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
chemical_list: D008764:Methylmalonic Acid