Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Objectives@#. Because climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases. @*Methods@#. We obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the relief-based feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R2) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model. @*Results@#. Features with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients. @*Conclusion@#. We successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.

Genome Sequencing of Human Cytomegalovirus Strain LCW and Identification of Asia-specific Cluster

Human cytomegalovirus (HCMV) is a ubiquitous human pathogen and contains double stranded DNA genome with approximately 230 kbp. Molecular genomic studies of HCMV have been attempted in order to understand the pathogenesis and evolution of HCMV. However, studies on HCMV strains of Asian origin are limited. In this study, it was attempted to understand the genomics of HCMV isolated from Korea. Clinical strain LCW isolated from Korean patient was passaged in vitro cell culture, and subjected to next-generation sequencing. Complete genome sequence was obtained and compared with other HCMV strains. The LCW genome was found to contain 170 open reading frames (ORFs) and two ORF (RL5A and RL13) of the strain LCW were found to be truncated due to early stop codon. Phylogenetic analysis suggested that the strain LCW was closely related with Asian strains such as HCMV strains JHC and HAN. Common nucleotide sequences among the 3 Asian strains distinguishable from other strains were detected at 197 sites including 104 sites in ORFs.

Ossification of the Medial Clavicular Epiphysis on Chest Radiographs: Utility and Diagnostic Accuracy in Identifying Korean Adolescents and Young Adults under the Age of Majority

The aim of our study was to evaluate the utility and diagnostic accuracy of the ossification grade of medial clavicular epiphysis on chest radiographs for identifying Korean adolescents and young adults under the age of majority. Overall, 1,151 patients (age, 16-30) without any systemic disease and who underwent chest radiography were included for ossification grading. Two radiologists independently classified the ossification of the medial clavicular epiphysis from chest radiographs into five grades. The age distribution and inter-observer agreement on the ossification grade were assessed. The diagnostic accuracy of the averaged ossification grades for determining whether the patient is under the age of majority was analyzed by using receiver operating characteristic (ROC) curves. Two separate inexperienced radiologists assessed the ossification grade in a subgroup of the patients after reviewing the detailed descriptions and image atlases developed for ossification grading. The median value of the ossification grades increased with increasing age (from 16 to 30 years), and the trend was best fitted by a quadratic function (R-square, 0.978). The inter-observer agreements on the ossification grade were 0.420 (right) and 0.404 (left). The area under the ROC curve (AUC) was 0.922 (95% CI, 0.902-0.942). The averaged ossification scores of 2.62 and 4.37 provided 95% specificity for a person < 19 years of age and a person ≥ 19 years of age, respectively. A preliminary assessment by inexperienced radiologists resulted in an AUC of 0.860 (95% CI, 0.740-0.981). The age of majority in Korean adolescents and young adults can be estimated using chest radiographs.