Fetching the paper…
Reading the bibliography…
With recent achievements in tasks requiring context awareness, foundation models have been adopted to treat large-scale data from electronic health record (EHR) systems.
Index for rating diagnostic tests
Youden, W. J · 1950
Earlier work this paper cites.
Missing data: our view of the state of the art
Schafer, J. L. and Graham, J. W · 2002
Earlier work this paper cites.
Optimal cut-point and its corresponding youden index to discriminate individuals using pooled blood samples
Schisterman, E. F., Perkins, N. J., Liu, A., and Bondell, H · 2005
Earlier work this paper cites.
Personalized diabetes management: moving from algorithmic to individualized therapy
Subramanian, S. and Hirsch, I. B · 2014
Earlier work this paper cites.
Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Earlier work this paper cites.
Recurrent neural networks for multivariate time series with missing values
Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
Earlier work this paper cites.
Pre-training of graph augmented transformers for medication recommendation
Shang, J., Ma, T., Xiao, C., and Sun, J · 2019
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2019
Cited alongside, same era.
Bica, I., Alaa, A. M., Jordon, J., and van der Schaar, M · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
Later among the works it cites.
Offline reinforcement learning as one big sequence modeling problem
Janner, M., Li, Q., and Levine, S · 2021
Later among the works it cites.
Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., and Zhi, D · 2021
Later among the works it cites.
Transfer learning in electronic health records through clinical concept embedding
Solares, J. R. A., Zhu, Y., Hassaine, A., Rao, S., Li, Y., Mamouei, M., Canoy, D., Rahimi, K., and Salimi-Khorshidi, G · 2021
Later among the works it cites.
Multimodal biomedical ai
Acosta, J. N., Falcone, G. J., Rajpurkar, P., and Topol, E. J · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Henry, A., Dachapally, P. R., Pawar, S., and Chen, Y · 2020
Cited alongside, same era.
Tabtransformer: Tabular data modeling using contextual embeddings
Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z · 2020
Cited alongside, same era.
Behrt: transformer for electronic health records
Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., and Salimi-Khorshidi, G · 2020
Cited alongside, same era.
Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation
Powers, D. M · 2020
Cited alongside, same era.
An overview of clinical decision support systems: benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., and Kroeker, K. I · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
Cited alongside, same era.
Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I
Cited in the paper.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al
Cited in the paper.
Lee, K.-H., Nachum, O., Yang, M., Lee, L., Freeman, D., Xu, W., Guadarrama, S., Fischer, I., Jang, E., Michalewski, H., et al · 2022
Later among the works it cites.
Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark
Ling, Y., Upadhyaya, P., Chen, L., Jiang, X., and Kim, Y · 2022
Later among the works it cites.
Causal transformer for estimating counterfactual outcomes
Melnychuk, V., Frauen, D., and Feuerriegel, S · 2022
Later among the works it cites.
Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., et al · 2022
Later among the works it cites.