公司规模
Large Corporate
国家
- United States
产品
- SemanticPro Classify & Automate
技术栈
- Natural Language Understanding
实施规模
- Pilot projects
影响指标
- Digital Expertise
- Innovation Output
技术
- 分析与建模 - 自然语言处理 (NLP)
适用行业
- 药品
适用功能
- 产品研发
- 质量保证
用例
- 监管合规监控
- 对话机器人
服务
- 数据科学服务
关于客户
客户是一家全球生物制药公司。该公司不直接向消费者销售药物,因此无法监控其药物的使用情况。患者记录受到隐私限制,即使有记录,公司也无法重建针对哪种疾病开出的药物。该公司希望通过筛选社交媒体上的患者评论来收集有关标示外用药的情报。
挑战
这家生物制药公司面临着追踪其药品的说明书外使用情况的挑战。说明书外使用是指以 FDA 未指定的方式开具药品,占所有处方的 10% 到 20%。然而,其确切程度很难衡量,因为制药公司没有直接的反馈机制来追踪其产品用于哪些疾病。该公司无法监控其药品的使用情况,因为患者记录受到隐私限制,不允许公司重建哪种药物用于哪种疾病。该公司决定使用社交媒体,尤其是 Reddit,作为有关药物使用情况的知识来源。
解决方案
该公司向 Cortical.io 寻求帮助,利用其在开发基于自然语言理解 (NLU) 的解决方案方面的专业知识,开发出一种工具,能够克服社交媒体帖子固有的歧义和模糊措辞障碍,并在可供训练的帖子数量有限的情况下正确解释这些帖子。在项目的初始阶段,Cortical.io 基于 SemanticPro Classify & Automate 创建了一个原型,用于识别一组静态 Reddit 帖子中提及的符合和不符合标签的药物使用情况。利用 Cortical.io 基于含义的算法,该应用程序可以自动准确地过滤和分类 Reddit 消息并汇总结果。Cortical.io 使用公开信息为公司指定的每种示例药物训练分类器。
运营影响
数量效益
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