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
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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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Self-citationTemporal Phenotyping from Longitudinal Electronic Health Records
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Self-citationA framework for feature extraction from hospital medical data with applications in risk prediction
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Self-citationDiscovering hospital admission patterns using models learnt from electronic hospital records
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Research works printed but not linked: 1
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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
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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
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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.
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