The DOI matches its Crossref record; 21 of the 23 references it cites were found in a registry or an index.
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The long short-term memory of the system models the illness trajectory and healthcare processes of users via a time-stamped sequence of events and in this way allows capturing long-term dependencies [41].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [41]
In the citing paper
“Using the stored data, the framework of DeepCare can model disease progression, support intervention recommendation, and provide disease prognosis based on EMR databases.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [41]
In the citing paper
“DeepCare is an example of an AI-based platform for end-to-end processing of EMR data. It uses a deep dynamic memory neural network to read and store experiences and in memory cells.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [41]
Citations
Cites
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
A Novel Connectionist System for Unconstrained Handwriting Recognition
Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Horst Bunke, A., & Schmidhuber, J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.137 · Cited by 2,041
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680
Jensen, A. B., Moseley, P. L., Oprea, T. I., Ellesøe, S. G., Eriksson, R., Schmock, H., Jensen, P. B., Jensen, L. J., & Brunak, S. (2014) · Nature Communications · doi:10.1038/ncomms5022 · Cited by 407
A Nursing Model for Chronic Illness Management Based Upon the Trajectory Framework
Corbin, J. M., & Strauss, A. (1991) · Research and theory for nursing practice · doi:10.1891/0889-7182.5.3.155 · Cited by 400
A comparison of models for predicting early hospital readmissions
Futoma, J., Morris, J. D., & Lucas, J. E. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.05.016 · Cited by 294
Unsupervised learning of disease progression models
Wang, X., Sontag, D. A., & Wang, F. (2014) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623754 · Cited by 220
Research works printed but not linked: 1
- Graves, A.: Generating sequences with recurrent neural networks (2013). arXiv preprint arXiv:1308.0850 · arXiv
Cited by
Sorted by citations
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24271-9 · Cited by 2,370
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636
Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792
Metadata
- Authors
- Pham, T., Tran, T., Phung, D., & Venkatesh, S.
- Affiliations
- Deakin University (AU)
- Corresponding author
- Pham, Trang
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 30-41
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2255847468
- Cited by
- 269
- References
- 23
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 21 · 2023: 31 · 2022: 25 · 2021: 30 · 2020: 43 · 2019: 42 · 2018: 38 · 2017: 10 · 2016: 4
- FWCI
- 16.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- electronic health records, long short-term memory, disease progression modeling, readmission prediction, diabetes mellitus, medical risk prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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