ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Data-driven Sublanguage Analysis for Cancer Genomics Knowledge Modeling: Applications in Mining Oncological Genetics Information from Patients' Genetic Reports.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
- Health Conditions Associated with Alzheimer's Disease and Vascular Dementia.Annals of neurology · 2023Article
- Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine.Bioinformatics (Oxford, England) · 2022Article
- PO2RDF: representation of real-world data for precision oncology using resource description framework.BMC medical genomics · 2022Article
- Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.BMC medical informatics and decision making · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite an abundance of information in clinical genetic testing reports, information is oftentimes not well documented/utilized for decision making. Unstructured information in genetic reports can contribute to long-term patient management and future translational research. Thus, we proposed a knowledge model that could manage unstructured information in medical genetic reports and facilitate knowledge extraction, curation and updating. For this pilot study, we used a dataset including 1,565 cancer genetics reports of Mayo Clinic patients. We used a previously developed, data-driven discovery pipeline that involves both semantic annotation and co-occurrence association analysis to establish a knowledge model. We showed that compared to genetic reports, around 56% of testing results are missing or incomplete in the clinical notes. We built a genetic report knowledge model and highlighted four key semantic groups including "Genes and Gene Products" and "Treatments". Coverage of term annotation was 99.5%. Accuracies of term annotation and relationship extraction were 98.9% and 92.9% respectively.
Identifiers
32477695PMC7233104What 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.