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pro vyhledávání: '"Hier, Daniel B."'
Term normalization is the process of mapping a term from free text to a standardized concept and its machine-readable code in an ontology. Accurate normalization of terms that capture phenotypic differences between patients and diseases is critical t
Externí odkaz:
http://arxiv.org/abs/2409.13746
Large language models (LLMs) have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term definitions. In this work, we introduce a simplified retriever that enh
Externí odkaz:
http://arxiv.org/abs/2409.13744
Autor:
Hier, Daniel B., Munzir, S. Ilyas, Stahlfeld, Anne, Obafemi-Ajayi, Tayo, Carrithers, Michael D.
High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritan
Externí odkaz:
http://arxiv.org/abs/2408.01214
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advance
Externí odkaz:
http://arxiv.org/abs/2406.14757
Autor:
Hier, Daniel B., Obafemi-Ajayi, Tayo, Olbricht, Gayla R., Burns, Devin M., Petrenko, Sasha, Wunsch II, Donald C.
Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatt
Externí odkaz:
http://arxiv.org/abs/2403.20246
Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years
Externí odkaz:
http://arxiv.org/abs/2403.05920
Autor:
Petrenko, Sasha1 (AUTHOR) hierd@mst.edu, Hier, Daniel B.1,2 (AUTHOR), Bone, Mary A.3 (AUTHOR) mary.bone@drmarybone.com, Obafemi-Ajayi, Tayo4 (AUTHOR) tayoobafemiajayi@missouristate.edu, Timpson, Erik J.5 (AUTHOR) etimpson@kcnsc.doe.gov, Marsh, William E.5 (AUTHOR) wmarsh@kcnsc.doe.gov, Speight, Michael5 (AUTHOR), Wunsch II, Donald C.1 (AUTHOR)
Publikováno v:
Information (2078-2489). Mar2024, Vol. 15 Issue 3, p125. 30p.
Enhanced neurologic concept recognition using a named entity recognition model based on transformers
Publikováno v:
Frontiers in digital health. 4
Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and sym
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d110efb24038ec9fdea6aa2928f70820
https://pubmed.ncbi.nlm.nih.gov/36569804
https://pubmed.ncbi.nlm.nih.gov/36569804
Publikováno v:
Frontiers in aging neuroscience. 14
The cytoskeletal protein tau is implicated in the pathogenesis of Alzheimer's disease which is characterized by intra-neuronal neurofibrillary tangles containing abnormally phosphorylated insoluble tau. Levels of soluble tau are elevated in the brain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::690f723627b4e7f96411306ee65ac292
https://pubmed.ncbi.nlm.nih.gov/36437992
https://pubmed.ncbi.nlm.nih.gov/36437992