Analysis of similarity measure in the longitudinal study using improved longest common subsequence method for lung cancer

作者：Fang LiYing; Wan Min^*; Yu MingWei; Yan JianZhuo; Liu Zheng; Wang Pu

来源：Biomedical Signal Processing and Control, 2015, 15: 60-66.

DOI：10.1016/j.bspc.2014.09.010

• Longitudinal data
• Time-series
• Similarity measure
• Longest common subsequence
• Hierarchical clustering

Background: In clinical practice, longitudinal data can be used to find trend patterns of pathema progress, such as tumour progress, along a time axis. This kind of data can be treated as time-series data. The maximum common sub-sequence is the most common method for calculating similarity of time-series data; and each point is normally treated as having the same weight. However, not all points of data within the time series should be given the same importance. According to clinical experience, the later period sub-sequence (closer to death) has a more significant effect than earlier periods in a trend analysis.
Results: A weighted-similarity measure based on LCSS with Constraint Window (W-LCSS-CW) Method is proposed. The results obtained from the time-series data using different weighting factors are discussed. In a study of non-small cell lung cancer using time-series data, the relative evaluation method and external evaluation method were adopted to calculate cluster effect. The results show that the proposed method, W-LCSS-CVV, can improve clustering performance significantly. Clustering performance of various methods was performed using a comparison of (C-index/M-index). The proposed W-LCSS-CW Method was evaluated to 1.55 which was 37.02%, 48.01%, 49.64% higher than other common methods (Euclidean, DTW, STS) respectively.
Conclusions: The proposed W-LCSS-CW Method is recommended for monitoring time-series data of tumour patients because the incorporated weighting factor provides more convincing cluster results for medical assist support.

• 出版日期2015-1
• 单位北京工业大学