TY - GEN
T1 - Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field
AU - Lin, Shaofu
AU - Zhao, Yuanyuan
AU - Huang, Zhisheng
PY - 2019
Y1 - 2019
N2 - As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75% and 93.33% respectively.
AB - As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75% and 93.33% respectively.
KW - Conditional random field
KW - Electronic medical record for depression
KW - Named entity recognition
KW - Time representation regular expression
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U2 - 10.1007/978-3-030-37078-7_15
DO - 10.1007/978-3-030-37078-7_15
M3 - Conference contribution
AN - SCOPUS:85078537614
SN - 9783030370770
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 149
EP - 158
BT - Brain Informatics
A2 - Liang, Peipeng
A2 - Goel, Vinod
A2 - Shan, Chunlei
PB - Springer
T2 - 12th International Conference on Brain Informatics, BI 2019
Y2 - 13 December 2019 through 15 December 2019
ER -
