The physicians' biographical pages are essential in providing information about physicians' specialties. However, physicians may not have biographical pages or the current pages are not comprehensive. We hypothesize that physicians' specialty information can be mined from Electronic Medical Records (EMRs) of their patients. We proposed an automated physician specialty populating (PSP) system that analyzes physician-ascertained diagnoses in EMRs, aggregates them to an appropriate granularity based on the current biographical pages, and populates the biographical pages accordingly. In this study, we applied the system using EMR data from Mayo Clinic and evaluated the system using the current biographical pages regarding various ranking strategies. Preliminary results demonstrated that using EMR data is a scalable and systematic way to populate physicians' biographical pages.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543344 | PMC |
Publication Analysis
Top Keywords
Similar Publications
From Paper to Digital: Evaluating Electronic Medical Records and their Compliance with EMA Guidelines in European Clinical Trials.
Rev Recent Clin Trials
January 2025
Dipartimento Patologia e Cura del Bambino, Regina Margherita AOU Città della Salute e della Scienza di Torino, Presidio Infantile Regina Margherita, Turin, Italy.
Background: Over the past decade, there has been a significant shift from paper-based to digital medical record management, driven largely by advances in digital technology. This transition has led to widespread adoption of Electronic Medical Records (EMRs), with the expectation that paper documentation will soon be fully replaced. In response, the European Medicines Agency's "Guideline on Computerised Systems in Clinical Trials" outlines essential criteria for validated EMR systems to ensure data integrity and security, and sets standards for electronic source documents in clinical trials.
View Article and Find Full Text PDFExperience using conventional compared to ancestry-based population descriptors in clinical genomics laboratories.
Am J Hum Genet
January 2025
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Various scientific and professional groups, including the American Medical Association (AMA), American Society of Human Genetics (ASHG), American College of Medical Genetics (ACMG), and the National Academies of Sciences, Engineering, and Medicine (NASEM), have appropriately clarified that certain population descriptors, such as race and ethnicity, are social and cultural constructs with no basis in genetics. Nevertheless, these conventional population descriptors are routinely collected during the course of clinical genetic testing and may be used to interpret test results. Experts who have examined the use of population descriptors, both conventional and ancestry based, in human genetics and genomics have offered guidance on using these descriptors in research but not in clinical laboratory settings.
View Article and Find Full Text PDFAwaiting insurance coverage: Medicaid enrollment and post-acute care use after traumatic injury.
J Trauma Acute Care Surg
January 2025
From the Section of Trauma and Acute Care Surgery, Department of Surgery (D.N.H., J.S.H.), University of Chicago, Chicago, Illinois; Perelman School of Medicine (E.C.E., A.T.C., O.I.R., A.U.M., M.K.D., N.D.M., M.J.S., E.J.K.), Division of Trauma, Surgical Critical Care and Emergency Surgery (K.M.C., N.D.M., M.J.S., E.J.K.), University of Pennsylvania, Philadelphia, Pennsylvania; and Department of Surgery (L.M.K.), Stanford University, Stanford, California.
Background: Lack of insurance after traumatic injury is associated with decreased use of postacute care and poor outcomes. Insurance linkage programs enroll eligible patients in Medicaid at the time of an unplanned admission. We hypothesized that Medicaid enrollment would be associated with increased use of postacute care, but also with prolonged hospital length of stay (LOS) while awaiting insurance authorization.
View Article and Find Full Text PDFDecision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.
Parasit Vectors
January 2025
Faculty of Information Technology, Mutah University, Mutah, Jordan.
Background: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples.
View Article and Find Full Text PDFConstruction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical, Radiological, and Peripheral Immunological Features.
Int J Surg
January 2025
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection.
Materials And Methods: A total of 1,092 participants were enrolled from 16 centers.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!