ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011
Alignment and clustering of breast cancer patients by longitudinal treatment history.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.JCO clinical cancer informatics · 2020Pooled it
- An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review.JMIR medical informatics · 2021Article
- EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group.Yearbook of medical informatics · 2014Article
- Oncoshare: lessons learned from building an integrated multi-institutional database for comparative effectiveness research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Longitudinal treatment histories may offer valuable information about clinical practice patterns to the clinical researcher as part of data exploration, cohort identification, or discovery of potentially beneficial or harmful practices in the health care community. We present a novel approach to temporal clustering of patient treatment information based on the semantic similarity of longitudinal histories. Using combined breast cancer registry data from two neighboring health care institutions, we constructed a database of longitudinal treatment histories that included surgical procedures, radiation therapy, chemotherapy, and hormone replacement therapy. We then did pair-wise similarity comparisons of treatment histories, and used the similarity measures to cluster patients with machine learning methods. An evaluation of our results found that patients clustered on stage of breast cancer and type of treatment provided. We propose that this approach can be applied towards identification of similar cohorts, and for discovery of novel or anomalous clinical practice patterns.
Indexed as
Identifiers
22195133PMC3243175What 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.