Electronic medical records-based selection of gastric cancer treatment plans
10.3969/j.issn.1671-3982.2018.01.010
- VernacularTitle:基于电子病历的胃癌治疗方案辅助选择
- Author:
Dong XIA
1
;
Guo-Lei LI
;
Xian-Lai CHEN
Author Information
1. 中国科学院成都文献情报中心
- Keywords:
Segmented words;
Cluster analysis;
Bayes discrimination;
EMR;
Clinical decision-making support;
Gastric cancer
- From:Chinese Journal of Medical Library and Information Science
2018;27(2):63-68
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the effective text mining methods by mining the information in electronic medical records( EMR) in order to achieve their value in support of decision-making. Methods Two thousand and five hundred EMR of gastric cancer patients were randomly divided into training group ( n＝1500) and testing group( n＝1000) . The words in the text of EMR of training group were identified using dictionary in combination with statistical methods. The segmented words were clustered according to the co-occurrence frequency of each segmented word and the treatment plan extracted from EMR. The matched number of words in each cluster from the text of EMR of training group was recorded. A decision-making support model of Bayes discrimination function was established according to the matched number of words in each cluster from the text of EMR of training group and treatment plan to verify the EMR in training group and to evaluate the words segmenting methods and the discrimination model. Results Fifty randomly selected RME showed that the recall rate, accurate rate and F-1 value of segmented words were 74. 24%, 82.30% and 78.06% respectively. The accurate rate of the established discrimination model was 62% for the identification of EMR of testing group when the segmented words were clustered into 5 categories. Conclusion The efficiency of dictionary in combination with statistical methods is good for identifying words from the text of EMR. Cluster-based text mining of EMR can achieve the decision-making support value of EMR, but the accuracy of the established decision-making support model is not as high as expected. Further study is thus necessary to identify the words from the text of EMR and the process of segmented words in establishing the decision-making support model.
