Mining Patterns of Disease Progression: A Topic-Model-Based Approach | |
Zhang, Lingxiao ; Zhao, Junfeng ; Wang, Yasha ; Xie, Bing | |
2016 | |
关键词 | Clinical Informatics Computer Assisted Medical Decision Support Advanced Analytics and Big Data |
英文摘要 | Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM) algorithms, can automatically extract such knowledge from large collections of electronic medical records (EMR). However, EMR data are usually unorganized and highly noisy. Finding meaningful disease patterns often calls for manual manipulation such as cohort and feature selection on EMR data by medical professionals. In this paper, we propose a topic-model-based SPM approach to find disease progression patterns from diagnostic records. We improve the traditional SPM algorithms by filtering and grouping the diagnosis sequences according to different clinical topics. These topics represent certain clinical conditions with closely related diagnoses, and are detected without prior medical knowledge. The experiment on real-world EMR data shows that our approach is able to find meaningful progression patterns with less noises, and can help quickly identify interesting patterns related to a certain clinical condition with less human effort.; CPCI-S(ISTP); zhaojf@pku.edu.cn; 354-358; 228 |
语种 | 英语 |
出处 | Medical Informatics Europe (MIE) Conference at Conference on Health - Exploring Complexity (HEC) - An Interdisciplinary Systems Approach |
DOI标识 | 10.3233/978-1-61499-678-1-354 |
内容类型 | 其他 |
源URL | [http://ir.pku.edu.cn/handle/20.500.11897/459973] |
专题 | 软件与微电子学院 |
推荐引用方式 GB/T 7714 | Zhang, Lingxiao,Zhao, Junfeng,Wang, Yasha,et al. Mining Patterns of Disease Progression: A Topic-Model-Based Approach. 2016-01-01. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论