Fetching the paper…
Reading the bibliography…
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.
M. W. Berry, S. T. Dumais and G. W. O’Brien, Using linear algebra for intelligent information retrieval, SIAM Rev
1995
Earlier work this paper cites.
O. Bodenreider, The unified medical language system (umls): integrating biomedical terminology, Nucleic Acids Research
2004
Earlier work this paper cites.
F. Morin and Y. Bengio, Hierarchical probabilistic neural network language model, in AISTATS
2005
Earlier work this paper cites.
R. Řehůřek and P. Sojka, Software Framework for Topic Modelling with Large Corpora, in Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks
2010
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research
2011
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, in Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2
2013
Earlier work this paper cites.
P. Cronin, J. Greenwald, G. C. Crevensten, H. Chueh and A. Zai, Development and implementation of a real-time 30-day readmission predictive model, AMIA Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
2014
Earlier work this paper cites.
J. Wiens, J. Guttag and E. Horvitz, A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions, Journal of the American Medical Informatics Association
2014
Earlier work this paper cites.
J. Pennington, R. Socher and C. D. Manning, Glove: Global vectors for word representation, in Empirical Methods in Natural Language Processing (EMNLP)
2014
Earlier work this paper cites.
Y. Kim, Convolutional neural networks for sentence classification, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
2014
Earlier work this paper cites.
G. Varando, C. Bielza and P. Larrañaga, Expressive power of binary relevance and chain classifiers based on bayesian networks for multi-label classification, in Probabilistic Graphical Models
2014
Earlier work this paper cites.
B. A. Goldstein, A. M. Navar, M. J. Pencina and J. P. A. Ioannidis, Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review, Journal of the American Medical Informatics Association
2016
Earlier work this paper cites.
R. Miotto, L. Li, B. A. Kidd and J. T. Dudley, Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Sci Rep
2016
Earlier work this paper cites.
E. Choi, M. T. Bahadori, E. Searles, C. Coffey, M. Thompson, J. Bost, J. T and J. Sun, Multi-layer representation learning for medical concepts, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
2016
Earlier work this paper cites.
Y. Choi, C. Y. Chiu and D. Sontag, Learning low-dimensional representations of medical concepts, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart and J. Sun, Doctor ai: Predicting clinical events via recurrent neural networks, in Proceedings of the 1st Machine Learning for Healthcare Conference
2016
Cited alongside, same era.
E. Choi, A. Schuetz, W. F. Stewart and J. Sun, Using recurrent neural network models for early detection of heart failure onset, J Am Med Inform Assoc
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. B. Dhudasia, S. Mukhopadhyay and K. M. Puopolo, Implementation of the sepsis risk calculator at an academic birth hospital, Hospital Pediatrics
2018
Later among the works it cites.
A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. Liu, J. Marcus, M. Sun, P. Sundberg, H. Yee, K. Zhang, Y. Zhang, G. Flores, G. E. Duggan, J. Irvine, Q. Le, K. Litsch, A. Mossin, J. Tansuwan, D. Wang, J. Wexler, J. Wilson, D. Ludwig, S. L. Volchenboum, K. Chou, M. Pearson, S. Madabushi, N. H. Shah, A. J. Butte, M. D. Howell, C. Cui, G. S. Corrado and J. Dean, Scalable and accurate deep learning with electronic health records, npj Digital Medicine
2018
Later among the works it cites.
J. Howard and S. Ruder, Universal language model fine-tuning for text classification, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2018
Later among the works it cites.
J. Devlin, M. Chang, K. Lee and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, in NAACL-HLT
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Cheng, F. Wang, P. Zhang and J. Hu, Risk prediction with electronic health records: A deep learning approach, in Proceedings of the 2016 SIAM International Conference on Data Mining
2016
Cited alongside, same era.
T. Pham, T. Tran, D. Phung and S. Venkatesh, Deepcare: A deep dynamic memory model for predictive medicine, in Pacific-Asia Conference on Knowledge Discovery and Data Mining
2016
Cited alongside, same era.
P. Nguyen, T. Tran, N. Wickramasinghe and S. Venkatesh, Deepr: a convolutional net for medical records, IEEE journal of biomedical and health informatics
2016
Cited alongside, same era.
2016
Cited alongside, same era.
R. Miotto, L. Li, B. A. Kidd and J. T. Dudley, Deep patient: an unsupervised representation to predict the future of patients from the electronic health records, Scientific reports
2016
Cited alongside, same era.
A. Avati, K. Jung, S. Harman, L. Downing, A. Ng and N. H. Shah, Improving palliative care with deep learning, in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
2017
Cited alongside, same era.
S. Tamang, A. Milstein, H. T. Sørensen, L. Pedersen, L. Mackey, J.-R. Betterton, L. Janson and N. Shah, Predicting patient ‘cost blooms’ in denmark: a longitudinal population-based study, BMJ Open
2017
Cited alongside, same era.
D. W. Shimabukuro, C. W. Barton, M. D. Feldman, S. J. Mataraso and R. Das, Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial, BMJ Open Respiratory Research
2017
Cited alongside, same era.
2018
Later among the works it cites.
J. Zhang, K. Kowsari, J. H. Harrison, J. M. Lobo and L. E. Barnes, Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record, IEEE Access
2018
Later among the works it cites.
D. Shen, G. Wang, W. Wang, M. R. Min, Q. Su, Y. Zhang, C. Li, R. Henao and L. Carin, Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2018
Later among the works it cites.
2018
Later among the works it cites.
B. Norgeot, B. S. Glicksberg and A. J. Butte, A call for deep-learning healthcare, Nature Medicine
2019
Later among the works it cites.
J. Wiens, S. Saria, M. Sendak, M. Ghassemi, V. X. Liu, F. Doshi-Velez, K. Jung, K. Heller, D. Kale, M. Saeed, P. N. Ossorio, S. Thadaney-Israni and A. Goldenberg, Do no harm: a roadmap for responsible machine learning for health care, Nature Medicine
2019
Later among the works it cites.
J. M. Banda, A. Sarraju, F. Abbasi, J. Parizo, M. Pariani, H. Ison, E. Briskin, H. Wand, S. Dubois, K. Jung, S. A. Myers, D. J. Rader, J. B. Leader, M. F. Murray, K. D. Myers, K. Wilemon, N. H. Shah and J. W. Knowles, Finding missed cases of familial hypercholesterolemia in health systems using machine learning, npj Digital Medicine
2019
Later among the works it cites.
D. Chen, S. Liu, P. Kingsbury, S. Sohn, C. B. Sorlie, E. B. Haberman, J. M. Naessens, D. W. Larson and H. Liu, Deep learning and alternative learning strategies for retrospective real-world clinical data, Nature Digital Medicine
2019
Later among the works it cites.
S. Shilo, H. Rossman and E. Segal, Axes of a revolution: challenges and promises of big data in healthcare, Nature Medicine
2020
Closest in time.
S. K. M. G. M. N. K. C. W. R. Mark P. Sendak, Joshua D’Arcy and S. Balu, A path for translation of machine learning products into healthcare delivery, EMJ Innovations
2020
Closest in time.
S. S. Paulson, B. A. Dummett, J. Green, E. Scruth, V. Reyes and G. J. Escobar, What do we do after the pilot is done? implementation of a hospital early warning system at scale, The Joint Commission Journal on Quality and Patient Safety
2020
Closest in time.
K. Clark, M.-T. Luong, Q. V. Le and C. D. Manning, Electra: Pre-training text encoders as discriminators rather than generators, in International Conference on Learning Representations
2020
Closest in time.