使用机器学习预测慢性疾病市场中LOE之后的品牌份额
1Department of Industrial and Data Engineering, Hongik University, Seoul, 04066, Republic of Korea.
概括
通过先进的机器学习模型,在失去独家性 (LOE) 后预测药品销售得到了改进. 这些模型准确地预测市场份额,甚至捕捉了传统方法错过的罕见份额回收.
科学领域:
- 药品市场分析 药品市场分析
- 计算流行病学计算流行病学
- 卫生经济学 卫生经济学
背景情况:
- 品牌药物的独家权丧失 (LOE) 会导致仿制药的快速竞争和市场份额的下降.
- 现有的预测模型经常使用简单的衰变曲线,面临数据稀缺性,低准确性和无法预测股票反弹的挑战.
- 准确的LOE后预测对于专利持有人和仿制药制造商的战略规划至关重要.
研究的目的:
- 开发和评估先进的机器学习算法,用于预测在失去独家性 (LOE) 后的品牌份额.
- 为了比较各种模型的性能,包括神经网络和随机森林,与传统方法.
- 确定影响LOE后市场动态和股票波动的关键驱动因素.
主要方法:
- 汇集了韩国20年的慢性疾病LOE事件 (高血压,脂血,糖尿病) 的面板数据集.
- 评估了一系列预测算法,从经典机器学习到神经网络 (例如,N-BEATS,随机森林).
- 利用SHAP分析来解释模型预测,并确定重要的影响因素.
主要成果:
- 该N-BEATS模型在预测绝对品牌份额方面取得了很高的准确性 (RMSE .034,MAPE .073).
- 随机森林模型在预测季度到季度的股票变化方面表现强 (RMSE .014,MAPE .147).
- 这些模型成功地捕捉了LOE后罕见的股票复苏事件,超过了传统的基准.
结论:
- 先进的机器学习模型为预测后LOE药物市场份额提供了一个优越的数据驱动框架.
- 仿制药的数量,LOE以来的时间,以及品牌持有者与当地分销商的合作伙伴关系是市场动态的关键驱动因素.
- 这些发现使制药行业的生命周期和市场准入规划能够更有效地实现战略调整.
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