Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial

Autor: Ian J. Saldanha, Christopher H. Schmid, Joseph Lau, Kay Dickersin, Jesse A. Berlin, Jens Jap, Bryant T. Smith, Simona Carini, Wiley Chan, Berry De Bruijn, Byron C. Wallace, Susan M. Hutfless, Ida Sim, M. Hassan Murad, Sandra A. Walsh, Elizabeth J. Whamond, Tianjing Li

Publikováno v: Systematic Reviews, Vol 5, Iss 1, Pp 1-11 (2016)

Abstract Background Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a no

Externí odkaz: https://doaj.org/article/3b73b29a64e8480db248cf2deb722103

Global Public Health Surveillance Using Media Reports: Redesigning GPHIN

Autor: Dave, Carter, Marta, Stojanovic, Philip, Hachey, Kevin, Fournier, Simon, Rodier, Yunli, Wang, Berry, De Bruijn

Publikováno v: Studies in health technology and informatics. 270

Global public health surveillance relies on reporting structures and transmission of trustworthy health reports. But in practice, these processes may not always be fast enough, or are hindered by procedural, technical, or political barriers. GPHIN, t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::8142f4c8bacf736654357525247eb32b
https://pubmed.ncbi.nlm.nih.gov/32570501

Recognizing UMLS semantic types with deep learning

Autor: Berry de Bruijn, Khaldoun Zine El Abidine, Isar Nejadgholi, Astha LaPlante, Muqun Li, Kathleen C. Fraser

Publikováno v: LOUHI@EMNLP

Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, Me

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19cb42618effc6ace694a9d09eb3e0bb
https://nrc-publications.canada.ca/eng/view/object/?id=de89b0eb-2023-4644-b6e1-a2f11d56a678

A randomized trial provided new evidence on the accuracy and efficiency of traditional vs. electronically annotated abstraction approaches in systematic reviews

Autor: Berry de Bruijn, Joseph K. Canner, Simona Carini, Christopher H. Schmid, Tianjing Li, Kay Dickersin, Wiley Chan, Jesse A. Berlin, Ian J. Saldanha, Bryant T Smith, Elizabeth J. Whamond, Vernal Branch, Byron C. Wallace, Susan Hutfless, Joseph Lau, Ida Sim, Sandra A. Walsh, M. Hassan Murad, Jens Jap

Publikováno v: Journal of clinical epidemiology. 115

Objectives Data Abstraction Assistant (DAA) is a software for linking items abstracted into a data collection form for a systematic review to their locations in a study report. We conducted a randomized cross-over trial that compared DAA-facilitated

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::adb9d911577f49b827025abecbca53c7
https://pubmed.ncbi.nlm.nih.gov/31302205

Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H) 2017 shared task

Autor: Jasper Friedrichs, Abeed Sarker, Filip Ginter, Maksim Belousov, Saif M. Mohammad, Ramakanth Kavuluru, Debanjan Mahata, Berry de Bruijn, Tung Tran, Anthony Rios, Graciela Gonzalez-Hernandez, Sifei Han, Kai Hakala, Svetlana Kiritchenko, Goran Nenadic, Farrokh Mehryary

Publikováno v: Sarkerb, A, Belousov, M, Friedrichs, J, Hakala, K, Kiritchenko, S, Mehryary, F, Han, S, Tran, T, Rios, A, Kavuluru, R, de Bruijn, B, Ginter, F, Mahata, D, Mohammad, S M, Nenadic, G & Gonzalez-Hernandez, G 2018, ' Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H) 2017 shared task ', Journal of the American Medical Informatics Association, vol. 25, no. 10, pp. 1274-1283 . https://doi.org/10.1093/jamia/ocy114
Journal of the American Medical Informatics Association : JAMIA

Objective: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f14d401f0df0cb9ceb28081a39ad4c33
https://www.research.manchester.ac.uk/portal/en/publications/data-and-systems-for-medicationrelated-text-classification-and-concept-normalization-from-twitter-insights-from-the-social-media-mining-for-health-smm4h-2017-shared-task(92eccc9d-0d02-49c4-b0b9-7fa138a2ca7d).html