Browsing by Author "Shen, Jia-Ying"

Now showing 1 - 2 of 2

A Two-Stage Learning Method for Response Prediction
(2015)
Chen, Kuan-Hsi
;
Ting, Zih-Yun
;
Shen, Jia-Ying
;
Hu, Yuh-Jyh
;
Liang, Tyne
Social networks have become a popular and powerful communication platform as the mobile technology evolves. To evaluate the influence of a message on a social network, response prediction is crucial in modeling the message propagation and interaction among users. To predict whether a new message will receive responses, we propose a two-stage learning method using a new set of features derived from the messages, the users and their responding behaviors. This method first clusters the messages, and then learns the different prediction models from the clusters respectively. The central argument for this two-stage strategy is that the classifiers trained separately from the clustered data sets can focus on particular types of data, reduce the effects of noise, and consequently have an overall higher predictive performance than a single classifier trained from the entire data set. We tested the proposed two-stage learner on Plurk, and compared it with other classifiers. The experimental results show that the two-stage learner outperformed the gradient boosting decision tree learner, the logistic function learner, and the support vector machine for not only the predictive accuracy, but also for the efficiency.
Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach
(2018-01-01)
Hu, Yuh-Jyh
;
Ku, Tien-Hsiung
;
Yang, Yu-Hung
;
Shen, Jia-Ying
;
分子醫學與生物工程研究所
Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.