OBJECTIVE: To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation 'Challenges in natural language processing for clinical data' for the task of classifying assertions associated with problem concepts extracted from patient records. MATERIALS AND METHODS: A combination of machine learning (conditional random field and maximum entropy) and rule-based (pattern matching) techniques was used to detect negation, speculation, and hypothetical and conditional information, as well as information associated with persons other than the patient. RESULTS: The best submission obtained an overall micro-averaged F-score of 0.9343. CONCLUSIONS: Using semantic attributes of concepts and information about document structure as features for statistical classification of assertions is a good way to leverage rule-based and statistical techniques. In this task, the choice of features may be more important than the choice of classifier algorithm.
OBJECTIVE: To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation 'Challenges in natural language processing for clinical data' for the task of classifying assertions associated with problem concepts extracted from patient records. MATERIALS AND METHODS: A combination of machine learning (conditional random field and maximum entropy) and rule-based (pattern matching) techniques was used to detect negation, speculation, and hypothetical and conditional information, as well as information associated with persons other than the patient. RESULTS: The best submission obtained an overall micro-averaged F-score of 0.9343. CONCLUSIONS: Using semantic attributes of concepts and information about document structure as features for statistical classification of assertions is a good way to leverage rule-based and statistical techniques. In this task, the choice of features may be more important than the choice of classifier algorithm.
Authors: Ben Wellner; Matt Huyck; Scott Mardis; John Aberdeen; Alex Morgan; Leonid Peshkin; Alex Yeh; Janet Hitzeman; Lynette Hirschman Journal: J Am Med Inform Assoc Date: 2007-06-28 Impact factor: 4.497
Authors: Peter L Elkin; Steven H Brown; Brent A Bauer; Casey S Husser; William Carruth; Larry R Bergstrom; Dietlind L Wahner-Roedler Journal: BMC Med Inform Decis Mak Date: 2005-05-05 Impact factor: 2.796
Authors: Sumithra Velupillai; Maria Skeppstedt; Maria Kvist; Danielle Mowery; Brian E Chapman; Hercules Dalianis; Wendy W Chapman Journal: Artif Intell Med Date: 2014-01-25 Impact factor: 5.326
Authors: Roy J Byrd; Steven R Steinhubl; Jimeng Sun; Shahram Ebadollahi; Walter F Stewart Journal: Int J Med Inform Date: 2013-01-11 Impact factor: 4.046
Authors: Jyotishman Pathak; Kent R Bailey; Calvin E Beebe; Steven Bethard; David C Carrell; Pei J 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 Masanz; Timothy Miller; Thomas A Oniki; Martha Palmer; Kevin J Peterson; Susan Rea; Guergana K Savova; Craig R Stancl; Sunghwan Sohn; Harold R Solbrig; Dale B Suesse; Cui Tao; David P Taylor; Les Westberg; Stephen Wu; Ning Zhuo; Christopher G Chute Journal: J Am Med Inform Assoc Date: 2013-11-04 Impact factor: 4.497
Authors: Justin A Strauss; Chun R Chao; Marilyn L Kwan; Syed A Ahmed; Joanne E Schottinger; Virginia P Quinn Journal: J Am Med Inform Assoc Date: 2012-07-21 Impact factor: 4.497