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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“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]
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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.
What counts as a claim ›How we measure confidence ›How we check this ›
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