Disseminating informatics knowledge and training the next generation of leaders.
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2014-NovEditorial
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2014-NovEditorial
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2014-003341
To present a series of experiments: (1) to evaluate the impact of pre-annotation on the speed of manual annotation of clinical trial announcements; and (2) to test for potential bias, if pre-annotation is utilized.
Author(s): Lingren, Todd, Deleger, Louise, Molnar, Katalin, Zhai, Haijun, Meinzen-Derr, Jareen, Kaiser, Megan, Stoutenborough, Laura, Li, Qi, Solti, Imre
DOI: 10.1136/amiajnl-2013-001837
The database of genotypes and phenotypes (dbGaP) developed by the National Center for Biotechnology Information (NCBI) is a resource that contains information on various genome-wide association studies (GWAS) and is currently available via NCBI's dbGaP Entrez interface. The database is an important resource, providing GWAS data that can be used for new exploratory research or cross-study validation by authorized users. However, finding studies relevant to a particular phenotype of interest [...]
Author(s): Doan, Son, Lin, Ko-Wei, Conway, Mike, Ohno-Machado, Lucila, Hsieh, Alex, Feupe, Stephanie Feudjio, Garland, Asher, Ross, Mindy K, Jiang, Xiaoqian, Farzaneh, Seena, Walker, Rebecca, Alipanah, Neda, Zhang, Jing, Xu, Hua, Kim, Hyeon-Eui
DOI: 10.1136/amiajnl-2013-001882
Recently, an important public debate emerged about the digital afterlife of any personal data stored in the cloud. Such debate brings also to attention the importance of transparent management of electronic health record (EHR) data of deceased patients. In this perspective paper, we look at legal and regulatory policies for EHR data post mortem. We analyze observational research situations using EHR data that do not require institutional review board approval [...]
Author(s): Huser, Vojtech, Cimino, James J
DOI: 10.1136/amiajnl-2013-002061
To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies.
Author(s): Gultepe, Eren, Green, Jeffrey P, Nguyen, Hien, Adams, Jason, Albertson, Timothy, Tagkopoulos, Ilias
DOI: 10.1136/amiajnl-2013-001815
This study aimed to reduce reliance on large training datasets in support vector machine (SVM)-based clinical text analysis by categorizing keyword features. An enhanced Mayo smoking status detection pipeline was deployed. We used a corpus of 709 annotated patient narratives. The pipeline was optimized for local data entry practice and lexicon. SVM classifier retraining used a grouped keyword approach for better efficiency. Accuracy, precision, and F-measure of the unaltered and [...]
Author(s): Khor, Richard, Yip, Wai-Kuan, Bressel, Mathias, Rose, William, Duchesne, Gillian, Foroudi, Farshad
DOI: 10.1136/amiajnl-2013-002090
Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders.
Author(s): Li, Ying, Salmasian, Hojjat, Vilar, Santiago, Chase, Herbert, Friedman, Carol, Wei, Ying
DOI: 10.1136/amiajnl-2013-001718
As part of the Heath Information Technology for Economic and Clinical Health (HITECH) Act, the Office of the National Coordinator for Health Information Technology (ONC) implemented its Workforce Development Program, which included initiatives to train health information technology (HIT) professionals in 12 workforce roles, half of them in community colleges. To achieve this, the ONC tasked five universities with established informatics programs with creating curricular materials that could be used [...]
Author(s): Mohan, Vishnu, Abbott, Patricia, Acteson, Shelby, Berner, Eta S, Devlin, Corkey, Hammond, William E, Kukafka, Rita, Hersh, William
DOI: 10.1136/amiajnl-2013-001683
To create a sense inventory of abbreviations and acronyms from clinical texts.
Author(s): Moon, Sungrim, Pakhomov, Serguei, Liu, Nathan, Ryan, James O, Melton, Genevieve B
DOI: 10.1136/amiajnl-2012-001506