ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012
Anomaly detection in clinical processes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules.Journal of cheminformatics · 2022Article
- Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection.Entropy (Basel, Switzerland) · 2020Article
- Outcome Prediction in Clinical Treatment Processes.Journal of medical systems · 2016Article
- A Method to Evaluate Critical Factors for Successful Implementation of Clinical Pathways.Applied clinical informatics · 2015Article
- Online treatment compliance checking for clinical pathways.Journal of medical systems · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meaningful anomalies in clinical processes may be related to caring performance or even the patient survival. It is imperative that the anomalies be timely detected such that useful and actionable knowledge of interest could be extracted to clinicians. Many previous approaches assume prior knowledge about the structure of clinical processes, using which anomalies are detected in a supervised manner. For a majority of clinical settings, however, clinical processes are complex, ad hoc, and even unknown a prior. In this paper, we investigate how to facilitate detection of anomalies in an unsupervised manner. An anomaly detection model is presented by applying a density-based clustering method on patient careflow logs. Using the learned model, it is possible to detect whether a particular patient careflow trace is anomalous with respect to normal traces in the logs. The approach has been validated over real data sets collected from a Chinese hospital.
Indexed as
Identifiers
23304307PMC3540475What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.