P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data

Breiman, L.: Random forests. Machine learning 45

2001

Earlier work this paper cites.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: The 3rd International Conference on Learning Representations, ICLR (2015)

2015

Earlier work this paper cites.

Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. pp. 785–794 (2016)

2016

Earlier work this paper cites.

Johnson, A.E., Pollard, T.J., Shen, L., Lehman, L.w.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., Mark, R.G.: Mimic-iii, a freely accessible critical care database. Scientific data 3

2016

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30

2017

Earlier work this paper cites.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

Original

2019

Earlier work this paper cites.

Chiew, C.J., Liu, N., Wong, T.H., Sim, Y.E., Abdullah, H.R.: Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission. Annals of surgery 272

2020

Cited alongside, same era.

Huang, X., Khetan, A., Cvitkovic, M., Karnin, Z.: Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678 (2020)

Original

2020

Cited alongside, same era.

Arik, S.Ö., Pfister, T.: Tabnet: Attentive interpretable tabular learning. In: Proceedings of the AAAI conference on artificial intelligence. vol. 35, pp. 6679–6687 (2021)

2021

Cited alongside, same era.

Chen, X., Huang, L., Liu, W., Shih, P.C., Bao, J.: Automatic surgery duration prediction using artificial neural networks. In: The 5th International Conference on Computer Science and Application Engineering. pp. 1–6 (2021)

2021

Cited alongside, same era.

Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems 34

2021

Later among the works it cites.

Gao, W., Wang, J., Zhou, L., Luo, Q., Lao, Y., Lyu, H., Guo, S.: Prediction of acute kidney injury in icu with gradient boosting decision tree algorithms. Computers in biology and medicine 140

2022

Later among the works it cites.

Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems 35

2022

Later among the works it cites.

Nistal-Nuño, B.: Developing machine learning models for prediction of mortality in the medical intensive care unit. Computer Methods and Programs in Biomedicine 216

2022

Later among the works it cites.

Wang, Z., Sun, J.: Transtab: Learning transferable tabular transformers across tables. Advances in Neural Information Processing Systems 35