Application of information technology: MedEx: a medication information extraction system for clinical narratives

By Hua Xu, Shane P. Stenner, S. Doan, Kevin B. Johnson, L. Waitman, Joshua Charles Denny. 2010

536 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zexian Zeng
2. Yu Deng
3. Ninghao Liu
4. Xiaoyu Li
5. Tristan Naumann
6. Katherine P. Liao
7. Xia Hu
8. Thomas A. Lasko

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “[184] developed medication information extraction system (MedEx) to extract medications and relations between them.”
• Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “[95] Kay Customized parsing to produce conceptual graph”
• A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011, unknown): “Most existing clinical NLP systems rely on vocabularies to recognize clinical entities such as diseases and medications”
• Extracting information from the text of electronic medical records to improve case detection: a systematic review (2016, methodology): “47 Medication information was extracted using MedEx (2 studies), 48 which produced RxNorm encoded medications, and FreePharma NLP (1 study).”
• Automatic lymphoma classification with sentence subgraph mining from pathology reports. (2014, methodology): “Other systems apply hand-crafted rules to extract pre-specified semantic relations, such as MedLEE [28], MedEx [29] and SemRep [30], or require supervised learning on pre-specified semantic relations, such as [31], thus are hard to adapt to new subdomains.”
• Bidirectional RNN for Medical Event Detection in Electronic Health Records (2016, unknown): “Zuofeng Li et al. (2010) extracted medication information us-ing CRFs.”
• Mining electronic health records: towards better research applications and clinical care (2012, background): “Another system, MedEx 31 , focuses on extracting detailed medication data from text.”
• Portability of an algorithm to identify rheumatoid arthritis in electronic health records (2012, methodology): “In contrast, all of Vanderbilt’s medications were derived using an NLP system called MedEx, which produced RxNorm-encoded medications along with signature information.(29) To ensure that these NLP-derived mentions represented actual medication use, we required each medication extract to contain a re”
• Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network (2015, unknown): “…machine learning (ML)-based methods, as well as hybrid methods have been reported, seven out of the top ten ranked systems were rule-based systems that rely on existing biomedical vocabularies to identify medication concepts [7], such as the MedEx system developed at Vanderbilt University [11].”
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, unknown): “Examples of systems that employ such databases and extract concepts from text are the KnowledgeMap Concept Identifier (KMCI) [44], MetaMap [45], Medlee [46], MedEx [47], and the cTAKES.”
• Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “[129] developed a rule-based system MedEx to extract medications and specific relations between medications and their associated strengths, routes and frequencies.”
• Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries (2015, methodology): “For example, Xu et al. [28] proposed a dictionary-based method for drug name recognition in discharge summaries.”
• Clinical Information Extraction Applications: A Literature Review (2017, methodology): “[141], has been applied in several studies, such as the application in [142].”
• Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “In fact, numerous publicly available NLP systems have been extended to perform the ADE detection task, including MedLEE [47], MetaMap [48], cTAKES [49], MedEx [50], and GATE [51].”