Application of PrefixSpan Algorithms for Disease Pattern Analysis

2016 International Computer Symposium (ICS) Pub Date : 2016-12-01 DOI:10.1109/ICS.2016.0062

Chi-Jane Chen, Tun-Wen Pai, Shih-Syun Lin, Chun-Chao Yeh, Min-Hui Liu, Chao-Hung Wang

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引用次数: 4

Abstract

PrefixSpan is a pattern-growth method for mining sequential patterns, and it is employed in this research for identifying disease trajectory patterns based on frequent subsequence analysis. One of the most beneficial features of this algorithm is the maintainable characteristics of original data order, especially for effectively and efficiently searching sequential patterns within a huge database. In this study, a medical database was adopted for disease transition analysis, and seven chronic diseases including diabetes, hyperlipidemia, hypertension, cerebrovascular disease, kidney disease, heart failure, and chronic obstructive pulmonary disease were mainly considered. By employing PrefixSpan algorithms, the statistical results of various combinations of chronic diseases with specific orders could be observed and compared. The results shows that patients suffered from hypertension (HTN) and followed by hyperlipidemia (DP) possess the most proportion among all subjects with a percentage of 37% (89,058/241,017). All statistical results of different combinations of seven chronic diseases, transition order, and proportional ranking were shown and discussed.

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PrefixSpan算法在疾病模式分析中的应用

PrefixSpan是一种挖掘序列模式的模式增长方法，本研究将其用于基于频繁子序列分析的疾病轨迹模式识别。该算法最有利的特点之一是原始数据顺序的可维护性，特别是对于在庞大的数据库中有效和高效地搜索顺序模式。本研究采用医学数据库进行疾病转移分析，主要考虑糖尿病、高脂血症、高血压、脑血管疾病、肾病、心力衰竭、慢性阻塞性肺疾病等7种慢性疾病。通过PrefixSpan算法，可以观察和比较慢性病按特定顺序的各种组合的统计结果。结果显示，在所有受试者中，高血压(HTN)患者所占比例最高，其次是高脂血症(DP)，占比为37%(89058 / 241017)。对7种慢性病不同组合的统计结果、过渡顺序和比例排序进行了显示和讨论。

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2016 International Computer Symposium (ICS)

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