Nilashi Mehrbakhsh, Ahmadi Hossein, Abumalloh Rabab Ali, Alrizq Mesfer, Alghamdi Abdullah, Alyami Sultan
UCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia.
Centre for Business Informatics and Industrial Management, UCSI Graduate Business School, UCSI University, Malaysia.
Heliyon. 2024 Sep 26;10(19):e38563. doi: 10.1016/j.heliyon.2024.e38563. eCollection 2024 Oct 15.
Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption.
乳腺癌是全球女性中最常被诊断出的危及生命的癌症。了解患者的用药经历对于改善治疗策略和治疗效果至关重要。在本研究中，我们利用患者的评论对乳腺癌药物进行知识发现。通过运用聚类、文本挖掘和回归技术，开发了一种新的机器学习方法。我们首先使用潜在狄利克雷分配（LDA）技术，从患者对乳腺癌药物的评论中发现患者经历的主要方面。我们还使用期望最大化（EM）算法，根据患者的总体满意度对数据进行分割。然后，我们使用前向逐步回归技术，在每个分割中找出患者经历的各个方面与药物疗效之间的关系。对乳腺癌药物的文本评论分析发现了8种主要副作用：肌肉骨骼影响、更年期影响、皮肤影响、代谢影响、胃肠道影响、神经和认知影响、呼吸影响和心血管影响。给出并讨论了结果。预计本研究的结果将为未来的患者提供有价值的见解和实用指导，帮助他们在乳腺癌药物消费方面做出明智的决策。
