Abstract
Clinical quantitative information commonly exists in electronic medical records (EMRs) and is essential for recording patients’ lab test or other characteristics in clinical notes. This study proposes an automated approach for extracting quantitative information from Chinese free-text EMR data including admission records, progress notes and ward-inspection records. The approach leverages pattern-learning combining with rule-based strategy to identify and extract clinical quantitative expressions. The experiments are based on 1,359 de-identified EMRs from the burn department of a domestic Grade-A Class-three hospital. The evaluation results present that our approach achieves a precision of 96.1%, a recall of 90.9%, and an F1-measure of 92.9%, demonstrating its effectiveness in clinical quantitative information extraction from EMR text.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hao, T., Wei, Y., Qiang, J., Wang, H., Lee, K.: The representation and extraction of quantitative information. In: ISO-ACL Workshop at the 12th International Conference on Computational Semantics (IWCS 2017), pp. 74–83 (2017)
Evans, D.A., Brownlow, N.D., Hersh, W.R., Campbell, E.M.: Automating concept identification in the electronic medical record: an experiment in extracting dosage information. In: A Conference of the American Medical Informatics Association, pp. 388–392 (1996)
Hassanpour, S., Langlotz, C.P.: Information extraction from multi-institutional radiology reports. Artif. Intell. Med. 66, 29–39 (2016)
Hao, T., Liu, H., Weng, C.: Valx: a system for extracting and structuring numeric lab test comparison statements from text. Methods Inf. Med. 55(3), 266–275 (2016)
Wang, Y., Wang, L., Rastegarmojarad, M., et al.: Clinical information extraction applications: a literature review. J. Biomed. Inform. 77, 34–49 (2017)
Mykowiecka, A., Marciniak, A.: Rule-based information extraction from patients’ clinical data. J. Biomed. Inform. 42(5), 923–936 (2009)
Mcdonald, C.J.: The barriers to electronic medical record systems and how to overcome them. J. Am. Med. Inform. Assoc. JAMIA 4(3), 213 (1997)
Wong, K.F., Li, W., Xu, R., Zhang, Z.S.: Introduction to Chinese natural language processing. Synth. Lect. Hum. Lang. Technol. 2(1), 1–148 (2009)
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucl. Acids Res. 32(Suppl_1), 267–270 (2004)
Liu, S., Ma, W., Moore, R., Ganesan, V., Nelson, S.: RxNorm: prescription for electronic drug information exchange. IT Prof. 7(5), 17–23 (2005)
Xu, Y., Wang, Y., Sun, J.T., Zhang, J., Tsujii, J., Chang, E.: Building large collections of Chinese and English medical terms from semi-structured and encyclopedia websites. PLoS ONE 8(7), e67526 (2013)
Jiang, Z., Zhao, F., Guan, Y.: Developing a linguistically annotated corpus of Chinese electronic medical record. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 307–310. IEEE (2014)
de Bruijn, B., Cherry, C., Kiritchenko, S., Martin, J., Zhu, X.: Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J. Am. Med. Inform. Assoc. 18(5), 557–562 (2011)
Fan, J.W., et al.: Syntactic parsing of clinical text: guideline and corpus development with handling ill-formed sentences. J. Am. Med. Inform. Assoc. 20(6), 1168–1177 (2013)
Uzuner, Ă–., Solti, I., Cadag, E.: Extracting medication information from clinical text. J. Am. Med. Inform. Assoc. JAMIA 17(5), 514 (2010)
Gold, S., Elhadad, N., Zhu, X., Cimino, J.J., Hripcsak, G.: Extracting structured medication event information from discharge summaries. In: AMIA Annual Symposium Proceedings/AMIA Symposium, vol. 2008, pp. 237–241 (2008)
Xu, H., Stenner, S.P., Doan, S., Johnson, K., Waitman, L., Denny, J.: MedEx: a medication information extraction system for clinical narratives. J. Am. Med. Inform. Assoc. 17(1), 19–24 (2010)
Patrick, J., Li, M.: High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. J. Am. Med. Inform. Assoc. 17(5), 524–527 (2010)
Xu, H., Doan, S., Birdwell, K.A., et al.: An automated approach to calculating the daily dose of tacrolimus in electronic health records. In: Amia Joint Summits on Translational Science Proceedings Amia Summit on Translational Science, vol. 2010, p. 71 (2010)
Garvin, J.H., et al.: Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure. J. Am. Med. Inform. Assoc. 19(5), 859–866 (2012)
Voorham, J., Denig, P.: Computerized extraction of information on the quality of diabetes care from free text in electronic patient records of general practitioners. J. Am. Med. Inform. Assoc. 14(3), 349–354 (2007)
Bigeard, E., Jouhet, V., Mougin, F., Thiessard, F., Grabar, N.: Automatic extraction of numerical values from unstructured data in EHRs. Stud. Health Technol. Inform. 210, 50–54 (2015)
Sohn, S., et al.: Analysis of cross-institutional medication description patterns in clinical narratives. Biomed. Inform. Insights 6(Suppl 1), 7–16 (2013)
Eiji, A., et al.: Extraction of adverse drug effects from clinical records. Stud. Health Technol. Inform. 160(Pt 1), 739 (2010)
Xu, D., et al.: Data-driven information extraction from Chinese electronic medical records. PLoS ONE 10(8), e0136270 (2015)
Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, vol. 1, pp. 63–70. Association for Computational Linguistics (2002)
Manning, C.D., Raghavan, P., SchĂĽtze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)
Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 61772146), Innovative School Project in Higher Education of Guangdong Province (No. YQ2015062), and Guangzhou Science Technology and Innovation Commission (No. 201803010063).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, S., Pan, X., Chen, B., Gao, D., Hao, T. (2018). An Automated Approach for Clinical Quantitative Information Extraction from Chinese Electronic Medical Records. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-01078-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01077-5
Online ISBN: 978-3-030-01078-2
eBook Packages: Computer ScienceComputer Science (R0)