Publications by authors named "Yu-Jui Lien"
Article Synopsis
- The study aimed to develop prediction models using basic factors to forecast outcomes for patients with sudden sensorineural hearing loss (SSNHL) based on a large group of 1,572 patients.
- Significant differences in recovery outcomes were linked to age, the duration of hearing loss before treatment, and the presence of vertigo, with younger patients and those treated sooner showing better results.
- The AdaBoost algorithm outperformed other models in accuracy and precision, highlighting its effectiveness for predicting SSNHL and suggesting that age and time since onset are critical factors for prognosis.
Background: Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment.
Purpose: To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting.