Data Processing
The data set was a combination of basic demographic data, clinical assessment data, and wearable device data. As shown in For cases that received a follow-up period longer than 50 days, we kept all the data labeled as positive and randomly sampled the remaining negative data points to bring the total number of data points to 50 in each case. For cases with a follow-up period shorter than 50 days, we included all of the data points. The data set was then divided into testing (25%) and training-validation (75%) sets, stratified by label and sex. To improve the model training, we upsampled the positive data points in the training-validation set. The entire flowchart of the study is shown in The combination of wearable device data and clinical assessments. The figure illustrates the process of data combination. Each column represents 1 data point. Each row represents a different kind of data. Days without any wearable device data uploaded were directly excluded from the analysis.Flowchart of the study. The figure illustrates the flowchart of the entire study from data collection and data processing to the training and evaluation of machine learning models. CV: cross-validation; ML: machine learning; NTU: National Taiwan University.