The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Knowledge-based temporal abstraction in clinical domains
Open accessAbstract
We have defined a knowledge-based framework for the creation of abstract, interval-based concepts from time-stamped clinical data, the knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes its task into five subtasks; for each subtask we propose a formal solving mechanism. Our framework emphasizes explicit representation of knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse and sharing. The RESUME system implements the KBTA method. We tested RESUME in several clinical-monitoring domains, including the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert diabetes-therapy temporal-abstraction knowledge. Two diabetes-therapy experts (including the first one) created temporal abstractions from about 800 points of diabetic-patients' data. RESUME generated about 80% of the abstractions agreed by both experts; about 97% of the generated abstractions were valid. We discuss the advantages and limitations of the current architecture.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clancey, W. J. (1985) · Artificial Intelligence · doi:10.1016/0004-3702(85)90016-5 · Cited by 880
Cited by
A Framework for Knowledge-Based Temporal Abstraction
Shahar, Y. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00025-2 · Cited by 461
EON: A Component-Based Approach to Automation of Protocol-Directed Therapy
Musen, M. A., Tu, S. W., Das, A. K., & Shahar, Y. (1996) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1996.97084511 · Cited by 346
Self-citationTemporal abstraction in intelligent clinical data analysis: A survey
Stacey, M., & McGregor, C. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2006.08.002 · Cited by 146
Hunter, J., Freer, Y., Gatt, A., Reiter, E., Sripada, S., & Sykes, C. (2012) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2012.09.002 · Cited by 127
Modeling asynchronous event sequences with RNNs
Wu, S., Liu, S., Sohn, S., Moon, S., Wi, C.-I., Juhn, Y., & Liu, H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.05.016 · Cited by 45
Metadata
- Authors
- Shahar, Y., & Musen, M. A.
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 1996-07
- Volume
- 8
- Issue
- 3
- Pages
- 267-298
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W1967931674
- Cited by
- 313
- References
- 1
- FWCI
- 53.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Knowledge-based temporal abstraction in clinical domains. https://app.cochat.ai/papers/10.1016/0933-3657(95)00036-4
- The paper
- Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine. https://doi.org/10.1016/0933-3657(95)00036-4
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
