图 书 馆
学习使用数字依从性数据为结核病患者制定干预措施Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data |
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| 课程网址: | http://videolectures.net/kdd2019_killian_wilder_sharma/ |
| 主讲教师: | Jackson A Killian |
| 开课单位: | 南加州大学 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in three different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers. |
| 关 键 词: | 学习使用数字; 依从性数据; 为结核病患者制定干预措施 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-16：cyh |
| 最后编审: | 2022-09-19：cyh |
| 阅读次数: | 37 |