A salient problem in informatics?
Author(s): Schleyer, Titus
DOI: 10.1197/jamia.M2752
Author(s): Schleyer, Titus
DOI: 10.1197/jamia.M2752
Author(s): Brennan, Patricia Flatley
DOI: 10.1197/jamia.m2691
Author(s): Chute, Christopher G
DOI: 10.1197/jamia.m2693
We conducted a reliability study comparing single data entry (SE) into a Microsoft Excel spreadsheet to entry using the existing forms (EF) feature of the Teleforms software system, in which optical character recognition is used to capture data off of paper forms designed in non-Teleforms software programs. We compared the transcription of data from multiple paper forms from over 100 research participants representing almost 20,000 data entry fields. Error rates [...]
Author(s): Wahi, Monika M, Parks, David V, Skeate, Robert C, Goldin, Steven B
DOI: 10.1197/jamia.M2381
Author(s): Staggers, Nancy, Brennan, Patricia Flatley
DOI: 10.1197/j.jamia.m2529
The anonymization of medical records is of great importance in the human life sciences because a de-identified text can be made publicly available for non-hospital researchers as well, to facilitate research on human diseases. Here the authors have developed a de-identification model that can successfully remove personal health information (PHI) from discharge records to make them conform to the guidelines of the Health Information Portability and Accountability Act.
Author(s): Szarvas, György, Farkas, Richárd, Busa-Fekete, Róbert
DOI: 10.1197/j.jamia.M2441
To characterize global structural features of large-scale biomedical terminologies using currently emerging statistical approaches.
Author(s): Bales, Michael E, Lussier, Yves A, Johnson, Stephen B
DOI: 10.1197/jamia.M2080
Cancer staging provides a basis for planning clinical management, but also allows for meaningful analysis of cancer outcomes and evaluation of cancer care services. Despite this, stage data in cancer registries is often incomplete, inaccurate, or simply not collected. This article describes a prototype software system (Cancer Stage Interpretation System, CSIS) that automatically extracts cancer staging information from medical reports. The system uses text classification techniques to train support vector [...]
Author(s): McCowan, Iain A, Moore, Darren C, Nguyen, Anthony N, Bowman, Rayleen V, Clarke, Belinda E, Duhig, Edwina E, Fry, Mary-Jane
DOI: 10.1197/jamia.M2130
To determine the accuracy of self-reported information from patients and families for use in a disease surveillance system.
Author(s): Bourgeois, Florence T, Porter, Stephen C, Valim, Clarissa, Jackson, Tiffany, Cook, E Francis, Mandl, Kenneth D
DOI: 10.1197/jamia.M2134
We implemented an automated vaccine adverse event surveillance and reporting system based in an ambulatory electronic medical record to improve underreporting and incomplete reporting that prevails in spontaneous systems. This automated system flags potential vaccine adverse events for the clinician when a diagnosis is entered, prompts clinicians to consider the vaccine as a cause of the condition, and facilitates reporting of suspected adverse events to the Vaccine Adverse Event Reporting [...]
Author(s): Hinrichsen, Virginia L, Kruskal, Benjamin, O'Brien, Megan A, Lieu, Tracy A, Platt, Richard
DOI: 10.1197/jamia.M2232