Abstract
Word embeddings are a useful tool for extracting knowledge from the free-form text contained in electronic health records, but it has become commonplace to train such word embeddings on data that do not accurately reflect how language is used in a healthcare context. We use prediction of medical codes as an example application to compare the accuracy of word embeddings trained on health corpora to those trained on more general collections of text. It is shown that both an increase in embedding dimensionality and an increase in the volume of health-related training data improves prediction accuracy. We also present a comparison to the traditional bag-of-words feature representation, demonstrating that in many cases, this conceptually simple method for representing text results in superior accuracy to that of word embeddings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Training was run on a 4 core Intel i7–6700K CPU @ 4.00 GHz with 64 GB of RAM.
References
Beam, A.L., et al.: Clinical concept embeddings learned from massive sources of multimodal medical data. arXiv preprint arXiv:1804.01486 (2018)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
Cao, Y., Huang, L., Ji, H., Chen, X., Li, J.: Bridge text and knowledge by learning multi-prototype entity mention embedding. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1623–1633 (2017)
Chen, Q., Peng, Y., Lu, Z.: BioSentVec: creating sentence embeddings for biomedical texts. In: 7th IEEE International Conference on Healthcare Informatics (2019)
Choi, E., Schuetz, A., Stewart, W.F., Sun, J.: Using recurrent neural network models for early detection of heart failure onset. J. Am. Med. Inform. Assoc. JAMIA 24(2), 361–370 (2017). https://doi.org/10.1093/jamia/ocw112
Choi, Y., Chiu, C.Y.I., Sontag, D.: Learning low-dimensional representations of medical concepts. AMIA Summits on Transl. Sci. Proc. 41–50 (2016)
MIT Critical Data: Secondary Analysis of Electronic Health Records. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43742-2_30
Goldberg, Y.: Neural network methods for natural language processing: Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954). https://doi.org/10.1080/00437956.1954.11659520
Jagannatha, A.N., Yu, H.: Bidirectional RNN for medical event detection in electronic health records. In: North American Chapter Meeting, pp. 473–482. Association for Computational Linguistics (2016)
Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395 (2012)
Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext.zip: compressing text classification models. arXiv preprint arXiv:1612.03651 (2016)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
MencÃa, E.L., De Melo, G., Nam, J.: Medical concept embeddings via labeled background corpora. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 4629–4636 (2016)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Pakhomov, S.V., Finley, G., McEwan, R., Wang, Y., Melton, G.B.: Corpus domain effects on distributional semantic modeling of medical terms. Bioinformatics 32(23), 3635–3644 (2016)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmark of deep learning models on large healthcare mimic datasets. arXiv preprint arXiv:1710.08531 (2017)
Roberts, K., et al.: Overview of the TREC 2017 precision medicine track. NIST Special Publication, pp. 500–324 (2017)
Shi, H., Xie, P., Hu, Z., Zhang, M., Xing, E.P.: Towards automated ICD coding using deep learning. arXiv preprint arXiv:1711.04075 (2017)
Witten, I., Frank, E., Hall, M., Pal, C.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann Publishers Inc., San Francisco (2016)
Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. arXiv preprint arXiv:1601.01343 (2016)
Zhang, Y., Chen, Q., Yang, Z., Lin, H., Lu, Z.: BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci. Data 6(1), 52 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yogarajan, V., Gouk, H., Smith, T., Mayo, M., Pfahringer, B. (2020). Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-41964-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41963-9
Online ISBN: 978-3-030-41964-6
eBook Packages: Computer ScienceComputer Science (R0)