SWoTTeD: an extension of tensor decomposition to temporal phenotyping
Author:
Funder
Fondation APHP
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10994-024-06545-8.pdf
Reference44 articles.
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2. Afshar, A., Perros, I., Park, H., Defilippi, C., Yan, X., Stewart, W., Ho, J., & Sun, J. (2020). TASTE: Temporal and static tensor factorization for phenotyping electronic health records. In Proceedings of the conference on health, inference, and learning (CHIL) (pp. 193–203).
3. Afshar, A., Yin, K., Yan, S., Qian, C., Ho, J. C., Park, H., & Sun, J. (2021). SWIFT: Scalable Wasserstein factorization for sparse nonnegative tensors. In Proceedings of the AAAI conference on artificial intelligence (pp. 6548–6556).
4. Ahn, D., Jang, J., & Kang, U. (2022). Time-aware tensor decomposition for sparse tensors. Journal of Machine Learning, 111(4), 1409–1430.
5. Ahuja, Y., Zou, Y., Verma, A., Buckeridge, D., & Li, Y. (2022). MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record. Journal of Biomedical Informatics, 134, 104190.
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