BMC Medical Informatics and Decision Making

By Qinghua Zeng, Sergey Goryachev, S. Weiss, M. Sordo, Shawn N. Murphy, Ross Lazarus. 2006

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “In the reviewed papers, SVMs constantly outperform other learning algorithms for computational phenotyping [27], [89], [99], [118], [127].”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “HITEx (Health Information Text Extraction) was also based on GATE and was developed to extract diagnoses and smoking status [93].”
Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. (2011, background): “Over the last three decades, a number of clinical NLP systems have been developed, including some earlier systems such as Medical Language Processing (MLP) system from the Linguistic String Project (LSP) [2, 3], MedLEE (Medical Language Extraction and Encoding System) [4-6], and SymText/MPlus [7-9],”
Enhancing clinical concept extraction with distributional semantics (2011, methodology): “A recent open-source tool HITEX [13] uses MetaMap to map concepts to UMLS strings.”
What can Natural Language Processing do for Clinical Decision Support? (2009, unknown): “finder [74].”
Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2) (2010, methodology): “In an i2b2 project database, new data can be derived from the raw EMR data in a multitude of different ways, such as through natural language processing, resulting in new, derived observations being created within the database.(15) Often the new observations are more accurate or precise than the ori”
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts (2011, methodology): “Some of these tools are MedLee, MetaMap, cTakes and HITEx ([29,38,39,40]).”
Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions (2011, background): “Adopting an existing NLP application or module is complicated—source code and documentation may be unavailable, and published descriptions may lack sufficient detail for reproducibility. Open source releases of clinical information extraction and retrieval systems have improved the opportunity to re”