Incorporating Causal Effects into Deep Learning Predictions on EHR Data

Miettinen, Olli. ”Confounding and effect-modification.” American Journal of Epidemiology 100.5 (1974): 350-353

1974

Earlier work this paper cites.

Wu, Jionglin, Jason Roy, and Walter F. Stewart. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. . Medical care (2010): S106-S113

2010

Earlier work this paper cites.

Judea, Pearl. ”An introduction to causal inference.” The International Journal of Biostatistics 6.2 (2010): 1-62

2010

Earlier work this paper cites.

Miotto, Riccardo, et al. ”Deep patient: an unsupervised representation to predict the future of patients from the electronic health records.” Scientific reports 6.1 (2016): 1-10

2016

Earlier work this paper cites.

Choi, et al. ”Doctor ai: Predicting clinical events via recurrent neural networks.” Machine learning for healthcare conference. PMLR, 2016

2016

Earlier work this paper cites.

Esteban, Cristóbal, et al. ”Predicting clinical events by combining static and dynamic information using recurrent neural networks.” 2016 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2016

2016

Earlier work this paper cites.

Choi, Edward, et al. ”Multi-layer representation learning for medical concepts.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016

2016

Earlier work this paper cites.

Pham, Trang, et al. ”Deepcare: A deep dynamic memory model for predictive medicine.” Pacific-Asia conference on knowledge discovery and data mining. Springer, Cham, 2016

2016

Earlier work this paper cites.

Miotto, Riccardo, et al. ”Deep patient: an unsupervised representation to predict the future of patients from the electronic health records.” Scientific reports 6.1 (2016): 1-10

2016

Earlier work this paper cites.

Choi, et al. Doctor ai: Predicting clinical events via recurrent neural networks. Machine learning for healthcare conference. PMLR, 2016

2016

Earlier work this paper cites.

Choi, Edward, et al. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. arXiv preprint arXiv:1608.05745 (2016)

Original

2016

Earlier work this paper cites.

Aczon, Melissa, et al. ”Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks.” arXiv preprint arXiv:1701.06675 (2017)

Original

2017

Cited alongside, same era.

Shickel, et al. ”Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis.” IEEE journal of biomedical and health informatics 22.5 (2017): 1589-1604

2017

Cited alongside, same era.

Wickramasinghe, Nilmini. ”Deepr: a convolutional net for medical records.” (2017)

2017

Cited alongside, same era.

Suresh, Harini, et al. ”Clinical intervention prediction and understanding using deep networks.” arXiv preprint arXiv:1705.08498 (2017)

Original

2017

Cited alongside, same era.

Choi, Edward, et al. ”Using recurrent neural network models for early detection of heart failure onset.” Journal of the American Medical Informatics Association 24.2 (2017): 361-370

Miotto, Riccardo, et al. Deep learning for healthcare: review, opportunities and challenges . Briefings in bioinformatics 19.6 (2018): 1236-1246

2018

Later among the works it cites.

Rajkomar, Alvin, et al. Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine 1.1 (2018): 1-10

2018

Later among the works it cites.

Rasmy, Laila, et al. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal of biomedical informatics 84 (2018): 11-16

2018

Later among the works it cites.

Glymour, Clark, Kun Zhang, and Peter Spirtes. ”Review of causal discovery methods based on graphical models.” Frontiers in genetics 10 (2019): 524

2019

Later among the works it cites.

Ke, Nan Rosemary, et al. ”Learning neural causal models from unknown interventions.” arXiv preprint arXiv:1910.01075 (2019)