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Peak ground acceleration (PGA) can be used to estimate the seismic intensity. However, using P-wave features to estimate PGA is a challenging task. One of the reasons for that is that a seismic wave commonly undergoes modification due to various site effects, consequently leading to uncertainty in the predicted PGA. In order to accommodate site effects using site parameters together with P-wave parameters, this paper takes advantage of machine learning to consider multiple parameters simultaneously. Several artificial neural network (ANN) models considering different site effect parameters are constructed. The performances of these ANN models were investigated and compared. In total, 53531 ground motion data obtained from the Taiwan Strong Motion Instrumentation Program were utilized to develop the proposed approach. It was found that the proposed ANN model with horizontal-to-vertical spectral ratio parameters effectively reduces the error of the estimated PGA when compared with either the ANN model without site parameters or the ANN model with other site parameters.

BackgroundHepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. ObjectiveThe aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year. MethodsPatients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works ResultsWe included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively. ConclusionsThe findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records.