关于乳腺癌药物的患者评论的知识发现:使用机器学习技术对副作用的细分
Mehrbakhsh Nilashi1,2, Hossein Ahmadi3, Rabab Ali Abumalloh4
1UCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia.
Heliyon
|October 21, 2024
概括
这项研究使用机器学习分析了乳腺癌药物评价,以确定关键的患者体验和副作用. 研究结果为选择乳腺癌药物的患者提供了见解.
科学领域:
- 在瘤学瘤学.
- 药物监督 药物监督 药物监督
- 数据科学数据科学数据科学
背景情况:
- 乳腺癌是全球女性的主要健康问题.
- 了解患者的药物经验对于治疗优化至关重要.
- 患者评论提供了一个有价值的,但未得到充分利用的数据来源.
研究的目的:
- 在乳腺癌药物评价中应用机器学习来发现知识.
- 确定患者体验的关键方面及其与药物有效性的关系.
- 为了分类乳腺癌患者报告的常见副作用.
主要方法:
- 潜在的迪里克莱特分配 (LDA) 用于对患者评价的主题建模.
- 基于满意度的数据细分的预期最大化 (EM) 算法.
- 前进回归回归以将患者的体验与药物的有效性相关联.
主要成果:
- 确定了8个主要类别的乳腺癌药物副作用:肌肉骨,更年期,皮肤,代谢,胃肠道,神经/认知,呼吸和心血管.
- 建立了特定患者报告的经验和在满意度段内感知到的药物有效性之间的关系.
- 揭示了有关乳腺癌治疗的患者反的模式.
结论:
- 机器学习有效地从患者对乳腺癌药物的评论中提取有价值的见解.
- 确定的副作用类别为了解患者报告的不良事件提供了一个框架.
- 这些发现可以使患者能够对乳腺癌治疗选择做出更明智的决定.
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