Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Chris, Beesley"'
Autor:
Kenneth Gersing, Jasmin Phua, Shaun Grannis, Sara Rogovin, Saad Ljazouli, Sam Michael, Chris Beesley
Publikováno v:
International Journal of Population Data Science, Vol 9, Iss 5 (2024)
The National Clinical Cohort Collaborative (N3C) uses privacy-preserving record linkage (PPRL) to formulate longitudinal patient histories and for de-duplication across disparate data including electronic health records, administrative claims, and mo
Externí odkaz:
https://doaj.org/article/2a476e9a7ea944608b03d61c305e8f2b
Autor:
Anand Krishnan, Heidi Schmidt, C. Max Schmidt, Paul R. Dexter, Chris Beesley, Saeed Mehrabi, Joseph Kesterson, Mathew J. Palakal, Alexandra M. Roch
Publikováno v:
HPB. 17(5):447-453
Introduction As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, followup is often not performed. Pancreatic cysts may have a significant malignant potent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::568bfc7628d648e76868f573882a6493
Autor:
Chris Beesley, Paul R. Dexter, Anand Krishnan, Heidi Schmidt, Alexandra M. Roch, Saeed Mehrabi, Sunghwan Sohn, Mathew J. Palakal, Hongfang Liu, C. Max Schmidt, Joe Kesterson
Publikováno v:
Journal of Biomedical Informatics. 54:213-219
In Electronic Health Records (EHRs), much of valuable information regarding patients’ conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24ddc70ae59cb036fcfb27510f8af7fa
https://doi.org/10.1016/j.jbi.2015.02.010
https://doi.org/10.1016/j.jbi.2015.02.010
Autor:
Saeed, Mehrabi, Anand, Krishnan, Alexandra M, Roch, Heidi, Schmidt, DingCheng, Li, Joe, Kesterson, Chris, Beesley, Paul, Dexter, Max, Schmidt, Mathew, Palakal, Hongfang, Liu
Publikováno v:
Studies in health technology and informatics. 216
In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::65c2b5d3b99e078e88a17fba12915d17
https://pubmed.ncbi.nlm.nih.gov/26262122
https://pubmed.ncbi.nlm.nih.gov/26262122
Autor:
Saeed, Mehrabi, C Max, Schmidt, Joshua A, Waters, Chris, Beesley, Anand, Krishnan, Joe, Kesterson, Paul, Dexter, Mohammed A, Al-Haddad, William M, Tierney, Mathew, Palakal
Publikováno v:
Studies in health technology and informatics. 192
Pancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and their surveillance can help to diagnose the disease in earlier stages. In this retrospectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::f2c3d52903f7de1b41b4fbb9ff9383b5
https://pubmed.ncbi.nlm.nih.gov/23920672
https://pubmed.ncbi.nlm.nih.gov/23920672
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