Abstract
The process of developing systematic reviews is a well established method of collecting evidence from publications, where it follows a predefined and explicit protocol design to promote rigour, transparency and repeatability. The process is manual and involves lot of time and needs expertise. The aim of this work is to build an effective framework using machine learning techniques to partially automate the process of systematic literature review by extracting required data elements of anxiety outcome measures. A framework is thus proposed that initially builds a training corpus by extracting different data elements related to anxiety outcome measures from relevant publications. The publications are retrieved from Medline, EMBASE, CINAHL, AHMED and Pyscinfo following a given set of rules defined by a research group in the United Kingdom reviewing comfort interventions in health care. Subsequently, the method trains a machine learning classifier using this training corpus to extract the desired data elements from new publications. The experiments are conducted on 48 publications containing anxiety outcome measures with an aim to automatically extract the sentences stating the mean and standard deviation of the measures of outcomes of different types of interventions to lessen anxiety. The experimental results show that the recall and precision of the proposed method using random forest classifier are respectively 100% and 83%, which indicates that the method is able to extract all required data elements.
S. Goswami and S. Pal have equal contribution in this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basu, T., et al.: A novel framework to expedite systematic reviews by automatically building information extraction training corpora. arXiv preprint arXiv:1606.06424 (2016)
Jonnalagadda, S.R., Goyal, P., Huffman, M.D.: Automating data extraction in systematic reviews: a systematic review. Syst. Rev. 4(1), 78 (2015)
Goldsworthy, S.D., Tuke, K., Latour, J.M.: A focus group consultation round exploring patient experiences of comfort during radiotherapy for head and neck cancer. J. Radiother. Pract. 15(2), 143–149 (2016)
Basu, T., Murthy, C.: A supervised term selection technique for effective text categorization. Int. J. Mach. Learn. Cybern. 7(5), 877–892 (2016)
Yadav, V., Bethard, S.: A survey on recent advances in named entity recognition from deep learning models. In: Proceedings of the International Conference on Computational Linguistics (COLING), pp. 2145–2158 (2018)
Uzuner, Ö., Luo, Y., Szolovits, P.: Evaluating the state-of-the-art in automatic de-identification. J. Am. Med. Inform. Assoc. 14(5), 550–563 (2007)
Uzuner, Ö., Solti, I., Cadag, E.: Extracting medication information from clinical text. J. Am. Med. Inform. Assoc. 17(5), 514–518 (2010)
Halgrim, S.R., Xia, F., Solti, I., Cadag, E., Uzuner, Ö.: A cascade of classifiers for extracting medication information from discharge summaries. J. Biomed. Semant. 2(3), S2 (2011)
Stubbs, A., Kotfila, C., Uzuner, Ö.: Automated systems for the de-identification of longitudinal clinical narratives: overview of 2014 i2b2/UTHealth shared task Track 1. J. Biomed. Inform. 58, S11–S19 (2015)
Stubbs, A., Filannino, M., Uzuner, Ö.: De-identification of psychiatric intake records: overview of 2016 CEGS N-GRID shared tasks Track 1. J. Biomed. Inform. 75, S4–S18 (2017)
Gobbel, G.T., et al.: Development and evaluation of raptat: a machine learning system for concept mapping of phrases from medical narratives. J. Biomed. Inform. 48, 54–65 (2014)
Zhang, B., Lu, M., Fang, Y.: A feature-enhanced entity recognition method for Chinese electronic medical records. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 9–14. IEEE (2018)
Goeuriot, L., et al.: Overview of the CLEF eHealth evaluation lab 2015. In: Mothe, J., et al. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 429–443. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24027-5_44
Dalianis, H., Velupillai, S.: De-identifying swedish clinical text-refinement of a gold standard and experiments with conditional random fields. J. Biomed. Semant. 1(1), 6 (2010)
Marshall, I.J., Kuiper, J., Banner, E., Wallace, B.C.: Automating biomedical evidence synthesis: RobotReviewer. In: Proceedings of the Conference. Association for Computational Linguistics. Meeting, vol. 2017, p. 7. NIH Public Access (2017)
Higgins, J.P.T., Green, S.: Cochrane Handbook for Systematic Reviews of Interventions, 5th edn. Cochrane Collaboration, London (2011)
Guntuku, S.C., Yaden, D.B., Kern, M.L., Ungar, L.H., Eichstaedt, J.C.: Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Behav. Sci. 18, 43–49 (2017)
De Choudhury, M., Counts, S., Horvitz, E.: Social media as a measurement tool of depression in populations. In: Proceedings of the Annual ACM Web Science Conference, pp. 47–56 (2013)
Shen, G., et al.: Depression detection via harvesting social media: a multimodal dictionary learning solution. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 3838–3844 (2017)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach.Learn. Res. 12, 2825–2830 (2011)
Basu, T., Murthy, C.A.: A feature selection method for improved document classification. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS (LNAI), vol. 7713, pp. 296–305. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35527-1_25
Trambert, R., Kowalski, M.O., Wu, B., Mehta, N., Friedman, P.: A randomized controlled trial provides evidence to support aromatherapy to minimize anxiety in women undergoing breast biopsy. Worldviews Evid.-Based Nurs. 14(5), 394–402 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Goswami, S., Pal, S., Goldsworthy, S., Basu, T. (2019). An Effective Machine Learning Framework for Data Elements Extraction from the Literature of Anxiety Outcome Measures to Build Systematic Review. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-20485-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20484-6
Online ISBN: 978-3-030-20485-3
eBook Packages: Computer ScienceComputer Science (R0)