The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Open accessAbstract
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts. To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts. Our approach, called cui2vec, attains state-of-the-art performance relative to previous methods in most instances. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings.
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Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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ImageNet: A large-scale hierarchical image database
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Efficient Estimation of Word Representations in Vector Space
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Universal Language Model Fine-tuning for Text Classification
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Big Data and Machine Learning in Health Care
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Self-citationAlsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop · doi:10.18653/v1/w19-1909 · Cited by 1,736
Cited by
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886
Graph embedding on biomedical networks: methods, applications and evaluations
Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S. M., Zhang, W., Zhang, P., & Sun, H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz718 · Cited by 386
Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying
Soroush, A., Glicksberg, B. S., Zimlichman, E., Barash, Y., Freeman, R., Charney, A. W., Nadkarni, G. N., & Klang, E. (2024) · NEJM AI · doi:10.1056/aidbp2300040 · Cited by 137
Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow
Pham, B., Jovanovic, J., Bagheri, E., Antony, J., Ashoor, H., Nguyen, T. T., Rios, P., Robson, R., Thomas, S. M., Watt, J., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01700-x · Cited by 63
Chen, Q., Lee, K., Yan, S., Kim, S., Wei, C.-H., & Lu, Z. (2020) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1007617 · Cited by 62
Metadata
- Authors
- Beam, A. L., Kompa, B., Schmaltz, A., Fried, I., Weber, G., Palmer, N., Shi, X., Cai, T., & Kohane, I. S.
- Venue
- Biocomputing 2020
- Publisher
- WORLD SCIENTIFIC
- Published
- 2019-11-27
- Volume
- 25
- Pages
- 295-306
- OpenAlex
- W2803274241
- Cited by
- 176
- References
- 8
- FWCI
- 11.32
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Beam, A. L., Kompa, B., Schmaltz, A., Fried, I., Weber, G., Palmer, N., Shi, X., Cai, T., & Kohane, I. S. (2019). Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. Biocomputing 2020. https://doi.org/10.1142/9789811215636_0027
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