Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium
Jyotishman Pathak,Kent R. Bailey,Calvin E. Beebe,Steven Bethard,David Carrell,Pei Chen,Dmitriy Dligach,Cory M. Endle,Lacey A. Hart,Peter J. Haug,Stanley M. Huff,Vinod C. Kaggal,Dingcheng Li,Hongfang Liu,Kyle Marchant,James J. Masanz,Timothy A. Miller,Thomas A. Oniki,Martha Palmer,Kevin J. Peterson,Susan Rea,Guergana Savova,Craig Stancl,Sunghwan Sohn,Harold R. Solbrig,Dale Suesse,Cui Tao,David P. Taylor,Les Westberg,Stephen Wu,Ning Zhuo,Christopher G. Chute +31 more
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TLDR
A data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions is developed and demonstrated by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density cholesterol test result during the measurement year was <100 mg/dL.About:
This article is published in Journal of the American Medical Informatics Association.The article was published on 2013-12-01 and is currently open access. It has received 107 citations till now. The article focuses on the topics: Data quality & Unstructured data.read more
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Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review
Kory Kreimeyer,Matthew Foster,Abhishek Pandey,Nina Arya,Gwendolyn Halford,Sandra F Jones,Richard A. Forshee,Mark Walderhaug,Taxiarchis Botsis +8 more
TL;DR: This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.
Journal ArticleDOI
Classification, Ontology, and Precision Medicine
TL;DR: Ontologies, Phenotypes, and Big Data Data-organizing methods have been in place for centuries, but very large data sets have come into being relatively recently.
Journal ArticleDOI
Clinical Natural Language Processing in languages other than English: opportunities and challenges
Aurélie Névéol,Hercules Dalianis,Sumithra Velupillai,Sumithra Velupillai,Guergana Savova,Pierre Zweigenbaum +5 more
TL;DR: This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English and identifies major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.
Journal ArticleDOI
Big data in medicine is driving big changes.
TL;DR: Current research that takes advantage of "Big Data" in health and biomedical informatics applications is summarized, highlighting ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas.
Journal ArticleDOI
Learning statistical models of phenotypes using noisy labeled training data.
Vibhu Agarwal,Tanya Podchiyska,Juan M. Banda,Veena V. Goel,Tiffany I. Leung,Evan P. Minty,Evan P. Minty,Timothy E. Sweeney,Elsie Gyang,Nigam H. Shah +9 more
TL;DR: The feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record is demonstrated, providing an alternative to manual labeling for creating training sets for statistical models of phenotypes.
References
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The “Meaningful Use” Regulation for Electronic Health Records
TL;DR: The widespread use of electronic health records (EHRs) in the United States is inevitable, but inevitability does not mean easy transition.
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Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
Guergana Savova,James J. Masanz,Philip V. Ogren,Jiaping Zheng,Sunghwan Sohn,Karin C Kipper-Schuler,Christopher G. Chute +6 more
TL;DR: The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text, and its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations.
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The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.
Catherine A. McCarty,Rex L. Chisholm,Christopher G. Chute,Iftikhar J. Kullo,Gail P. Jarvik,Eric B. Larson,Rongling Li,Daniel R. Masys,Marylyn D. Ritchie,Dan M. Roden,Jeffery P. Struewing,Wendy A. Wolf +11 more
TL;DR: By combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.
Journal ArticleDOI
A survey on ontology mapping
TL;DR: This paper presents ontology mapping categories, describes the characteristics of each category, compares these characteristics, and surveys tools, systems, and related work based on each category ofOntology mapping.
Journal ArticleDOI
Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data
Susan Rea,Jyotishman Pathak,Guergana Savova,Thomas A. Oniki,Les Westberg,Calvin E. Beebe,Cui Tao,Craig G. Parker,Peter J. Haug,Stanley M. Huff,Christopher G. Chute +10 more
TL;DR: One year into the design and development of the SHARPn framework, end to end data flow is demonstrated and the EHR data throughput demonstration showed success in normalizing native Ehr data, both structured and narrative, from two independent organizations and EHR systems.
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