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pro vyhledávání: '"Dobbins, Nicholas"'
Autor:
Dobbins, Nicholas J
Background: Biomedical entity normalization is critical to biomedical research because the richness of free-text clinical data, such as progress notes, can often be fully leveraged only after translating words and phrases into structured and coded re
Externí odkaz:
http://arxiv.org/abs/2405.15122
Autor:
Fu, Yujuan, Ramachandran, Giridhar Kaushik, Dobbins, Nicholas J, Park, Namu, Leu, Michael, Rosenberg, Abby R., Lybarger, Kevin, Xia, Fei, Uzuner, Ozlem, Yetisgen, Meliha
Social determinants of health (SDoH) play a critical role in shaping health outcomes, particularly in pediatric populations where interventions can have long-term implications. SDoH are frequently studied in the Electronic Health Record (EHR), which
Externí odkaz:
http://arxiv.org/abs/2404.00826
Autor:
Ramachandran, Giridhar Kaushik, Fu, Yujuan, Han, Bin, Lybarger, Kevin, Dobbins, Nicholas J, Uzuner, Özlem, Yetisgen, Meliha
Social determinants of health (SDOH) documented in the electronic health record through unstructured text are increasingly being studied to understand how SDOH impacts patient health outcomes. In this work, we utilize the Social History Annotation Co
Externí odkaz:
http://arxiv.org/abs/2306.07170
Autor:
Dobbins, Nicholas J, Han, Bin, Zhou, Weipeng, Lan, Kristine, Kim, H. Nina, Harrington, Robert, Uzuner, Ozlem, Yetisgen, Meliha
Publikováno v:
Journal of the American Medical Informatics Association, 2023;, ocad149
Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of
Externí odkaz:
http://arxiv.org/abs/2304.06203
Autor:
Lybarger, Kevin, Dobbins, Nicholas J, Long, Ritche, Singh, Angad, Wedgeworth, Patrick, Ozuner, Ozlem, Yetisgen, Meliha
Objective: Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH inf
Externí odkaz:
http://arxiv.org/abs/2212.07538
Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criteria are often most naturally described in free-text, using language fam
Externí odkaz:
http://arxiv.org/abs/2207.13757
Autor:
Yan, Yao, Yu, Thomas, Muenzen, Kathleen, Liu, Sijia, Boyle, Connor, Koslowski, George, Zheng, Jiaxin, Dobbins, Nicholas, Essien, Clement, Liu, Hongfang, Omberg, Larsson, Yestigen, Meliha, Taylor, Bradley, Eddy, James A, Guinney, Justin, Mooney, Sean, Schaffter, Thomas
Objective The evaluation of natural language processing (NLP) models for clinical text de-identification relies on the availability of clinical notes, which is often restricted due to privacy concerns. The NLP Sandbox is an approach for alleviating t
Externí odkaz:
http://arxiv.org/abs/2206.14181
Publikováno v:
AMIA Virtual Summits 2021
Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research. However, concerns about patient privacy and confidentiality limit t
Externí odkaz:
http://arxiv.org/abs/2102.11032
Objective: Neural network de-identification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-worl
Externí odkaz:
http://arxiv.org/abs/2102.08517
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