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A patient undergoes multiple examinations in each hospital stay, where each provides different facets of the health status.
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Lipton, Z.C., Kale, D., Wetzel, R.: Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. In: Proceedings of the 1st Machine Learning for Healthcare Conference. vol. 56, pp. 253–270 (18–19 Aug 2016), https://proceedings.mlr.press/v56/Lipton16.html
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Huang, S.C., Pareek, A., Seyyedi, S., Banerjee, I., Lungren, M.P.: Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. npj Digital Medicine 3
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Huang, S.C., Pareek, A., Zamanian, R., Banerjee, I., Lungren, M.P.: Multimodal fusion with deep neural networks for leveraging ct imaging and electronic health record: a case-study in pulmonary embolism detection. Scientific reports 10
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Duan, R., Gao, L., Gao, Y., Hu, Y., Xu, H., Huang, M., Song, K., Wang, H., Dong, Y., Jiang, C., et al.: Evaluation and comparison of multi-omics data integration methods for cancer subtyping. PLoS computational biology 17
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Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., Carreira, J.: Perceiver: General perception with iterative attention. In: International Conference on Machine Learning (ICML) (2021). https://doi.org/10.48550/2103.03206
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In: International Conference on Computer Vision (2021). https://doi.org/1048550/arXiv.2103.14030
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763 (2021)
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Acosta, J.N., Falcone, G.J., Rajpurkar, P., Topol, E.J.: Multimodal biomedical ai. Nature Medicine 28
2022
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Johnson, A.E.W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T.J., Hao, S., Moody, B., Gow, B., Lehman, L.w.H., Celi, L.A., Mark, R.G.: Mimic-iv, a freely accessible electronic health record dataset. Scientific Data 10
2023
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Khader, F., Kather, J.N., Müller-Franzes, G., Wang, T., Han, T., Arasteh, S.T., Hamesch, K., Bressem, K., Haarburger, C., Stegmaier, J., Kuhl, C., Nebelung, S., Truhn, D.: Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data. Nature Scientific Reports 13
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Li, Z., Li, S., Yan, X.: Time Series as Images: Vision Transformer for Irregularly Sampled Time Series. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS) (2023), https://openreview.net/forum?id=ZmeAoWQqe0
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Bluethgen, C., Chambon, P., Delbrouck, J.B., van der Sluijs, R., Połacin, M., Zambrano Chaves, J.M., Abraham, T.M., Purohit, S., Langlotz, C.P., Chaudhari, A.S.: A vision–language foundation model for the generation of realistic chest x-ray images. Nature Biomedical Engineering (2024). https://doi.org/10.1038/s41551-024-01246-y
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Rasekh, A., Heidari, R., Hosein Haji Mohammad Rezaie, A., Sharifi Sedeh, P., Ahmadi, Z., Mitra, P., Nejdl, W.: Robust fusion of time series and image data for improved multimodal clinical prediction. IEEE ACCESS 12
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2025
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