Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.

Importance: Early awareness of Kawasaki disease (KD) helps physicians administer appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD is challenging and relies largely on subjective diagnosis criteria. Objective: To develop a prediction model using machine learning with objective parameters to differentiate children with KD from other febrile children. Design, Setting, and Participants: This diagnostic study included 74 641 febrile children younger than 5 years who were recruited from 4 hospitals, including 2 medical centers and 2 regional hospitals, between January 1, 2010, and December 31, 2019. Statistical analysis was performed from October 2021 to February 2023. Main Outcomes and Measures: Demographic data and laboratory values from electronic medical records, including complete blood cell count with differential, urinalysis, and biochemistry, were collected as possible parameters. The primary outcome was whether the febrile children fulfilled the diagnostic criteria of KD. The supervised eXtreme Gradient Boosting (XGBoost) machine learning method was applied to establish a prediction model. The confusion matrix and likelihood ratio were used to evaluate the performance of the prediction model. Results: This study included a total of 1142 patients with KD (mean [SD] age, 1.1 [0.8] years; 687 male patients [60.2%]) and 73 499 febrile children (mean [SD] age, 1.6 [1.4] years; 41 465 male patients [56.4%]) comprising the control group. The KD group was predominantly male (odds ratio, 1.79; 95% CI, 1.55-2.06) with younger age (mean difference, -0.6 years [95% CI, -0.6 to -0.5 years]) compared with the control group. The prediction model's best performance in the testing set was able to achieve 92.5% sensitivity, 97.3% specificity, 34.5% positive predictive value, 99.9% negative predictive value, and a positive likelihood ratio of 34.0, which indicates outstanding performance. The area under the receiver operating characteristic curve of the prediction model was 0.980 (95% CI, 0.974-0.987). Conclusions and Relevance: This diagnostic study suggests that results of objective laboratory tests had the potential to be predictors of KD. Furthermore, these findings suggested that machine learning with XGBoost can help physicians differentiate children with KD from other febrile children in pediatric emergency departments with excellent sensitivity, specificity, and accuracy.

One-step fabrication of carbon nanotubes decorated with graphitic nanoflakes for energy storage systems.

We have used a bias-assisted microwave plasma chemical vapor deposition system to synthesize carbon nanotubes presenting graphitic nanoflakes, named coral-like carbon nanotubes, and well-aligned carbon nanotubes on carbon cloth substrates. Applying an external bias of -100 V led to the growth of well-aligned carbon nanotubes. In the absence of an external bias, the coral-like nanotubes presenting graphite nanoflakes were formed. The specific surface areas of the well-aligned and coral-like carbon nanotubes electrodes were 90.31 and 143.69 m2/g, respectively. In terms of energy storage, we estimated the capacitance of the coral-like carbon nanotube electrode to be ca. 194 F/g in an electrolyte of 1 M H2SO4. This value is almost double that of the well-aligned carbon nanotubes electrode (104 F/g), presumably because the presence of the carbon nanoflakes had a positive influence on the migration and adsorption of ions within the electrode. The fitting results indicated that the coral-like carbon nanotubes electrode behaved as a traditional electrochemical capacitor. Durability tests revealed that the coral-like carbon nanotube electrode was reliable, with a decay of 9% in capacitance over 1000 cycles.