JAMIA Open Article
Community abstracts: coming soon!
December 1, 2019
Author(s): Sarkar, Indra Neil
DOI: 10.1093/jamiaopen/ooz070
Author(s): Sarkar, Indra Neil
DOI: 10.1093/jamiaopen/ooz070
The Peace Corps' disease surveillance for Peace Corps Volunteers (PCVs) was incorporated into an electronic medical records (EMR) system in 2015. We evaluated this EMR-based surveillance system, focusing particularly on malaria as it is deadly but preventable.
Author(s): Davlantes, Elizabeth, Henderson, Susan, Ferguson, Rennie W, Lewis, Lauren, Tan, Kathrine R
DOI: 10.1093/jamiaopen/ooz047
Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challenge. We aim to develop a high throughput NLP architecture using the clinical Text Analysis and Knowledge Extraction System and present a predictive model use case.
Author(s): Afshar, Majid, Dligach, Dmitriy, Sharma, Brihat, Cai, Xiaoyuan, Boyda, Jason, Birch, Steven, Valdez, Daniel, Zelisko, Suzan, Joyce, Cara, Modave, François, Price, Ron
DOI: 10.1093/jamia/ocz068
Author(s): Gardner, Dr Rebekah L, Cooper, Emily, Haskell, Jacqueline, Harris, Daniel A, Poplau, Sara, Kroth, Philip J, Linzer, Mark
DOI: 10.1093/jamia/ocz077
Author(s):
DOI: 10.1093/jamia/ocz083
Author(s): Stubbs, Amber, Uzuner, Özlem
DOI: 10.1093/jamia/ocz174
Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).
Author(s): Liao, Katherine P, Sun, Jiehuan, Cai, Tianrun A, Link, Nicholas, Hong, Chuan, Huang, Jie, Huffman, Jennifer E, Gronsbell, Jessica, Zhang, Yichi, Ho, Yuk-Lam, Castro, Victor, Gainer, Vivian, Murphy, Shawn N, O'Donnell, Christopher J, Gaziano, J Michael, Cho, Kelly, Szolovits, Peter, Kohane, Isaac S, Yu, Sheng, Cai, Tianxi
DOI: 10.1093/jamia/ocz066
Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused on identifying which patients in a corpus of longitudinal medical records meet and do not meet identified selection criteria.
Author(s): Stubbs, Amber, Filannino, Michele, Soysal, Ergin, Henry, Samuel, Uzuner, Özlem
DOI: 10.1093/jamia/ocz163
The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment.
Author(s): Miller, Hailey N, Gleason, Kelly T, Juraschek, Stephen P, Plante, Timothy B, Lewis-Land, Cassie, Woods, Bonnie, Appel, Lawrence J, Ford, Daniel E, Dennison Himmelfarb, Cheryl R
DOI: 10.1093/jamia/ocz168
Electronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection. We aim to compare the cohort selection performance using Australian Medicines Terminology to Anatomical Therapeutic Chemical [...]
Author(s): Guo, Guan N, Jonnagaddala, Jitendra, Farshid, Sanjay, Huser, Vojtech, Reich, Christian, Liaw, Siaw-Teng
DOI: 10.1093/jamia/ocz143