Mining Patterns of Disease Progression: A Topic-Model-Based Approach | |
Zhang Lingxiao ; Zhao Junfeng ; Wang Yasha ; Xie Bing | |
刊名 | Studies in health technology and informatics |
2016 | |
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.; PubMed; 354-8; 228 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.pku.edu.cn/handle/20.500.11897/493108] |
专题 | 信息科学技术学院 |
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[J]. Studies in health technology and informatics,2016. |
APA | Zhang Lingxiao,Zhao Junfeng,Wang Yasha,&Xie Bing.(2016).Mining Patterns of Disease Progression: A Topic-Model-Based Approach.Studies in health technology and informatics. |
MLA | Zhang Lingxiao,et al."Mining Patterns of Disease Progression: A Topic-Model-Based Approach".Studies in health technology and informatics (2016). |
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