Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia.
Int J Environ Res Public Health. 2023 Feb 13;20(4):3289. doi: 10.3390/ijerph20043289.
Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.
骨质疏松症是一种严重的骨骼疾病，影响着全球许多人。已经有多种药物被用于治疗骨质疏松症。然而，这些药物可能会在患者身上引起严重的不良反应。药物不良反应是由药物使用引起的有害反应，仍然是许多国家导致死亡的主要原因之一。早期预测严重药物不良反应有助于挽救患者生命和降低医疗成本。分类方法常用于预测不良反应的严重程度。这些方法通常假设属性之间相互独立，但在实际应用中可能并不实用。本文提出了一种新的属性加权逻辑回归方法来预测药物不良反应的严重程度。我们的方法放宽了属性之间相互独立的假设。在从美国食品和药物管理局数据库中获得的骨质疏松症数据上进行了评估。结果表明，我们的方法在预测药物不良反应的严重程度方面具有更高的识别性能，并优于基线方法。
