.png){kind=icon}
返回
DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/JBHI.2018.2828028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Electronic health records have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of these data can prevent mistakes that may put patients' lives at risk. Nonetheless, to the best of our knowledge, there are no previous studies addressing the automatic detection of outlier prescriptions, regarding dosage and frequency. In this paper, we propose an unsupervised method, called density-distance-centrality (DDC), to detect potential outlier prescriptions. A dataset with 563 thousand prescribed medications was used to assess our proposed approach against different state-of-the-art techniques for outlier detection. In the experiments, our approach achieves better results in the task of overdose and underdose detection in medical prescriptions, compared to other methods applied to this problem. Additionally, most of the false positive instances detected by our algorithm were potential prescriptions errors.
Keyword:
Electronic health records
prescription errors
unsupervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
4.5K
被引数:
2.0W
机构
引用论文
.png){kind=icon}
A Flying Squirrel Search Optimization for MPPT Under Partial Shaded Photovoltaic System局部遮挡光伏系统MPPT的飞鼠搜索优化
PREAI
分享收藏A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery一种新的用于罕见处方副作用发现的半监督算法
OAAI
OAAI分享收藏
查看更多内容