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10.1136/amiajnl-2014-002694 http://scihub22266oqcxt.onion/10.1136/amiajnl-2014-002694 C4147615!4147615!24786209     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24786209&cmd=llinks): Failed to open stream: HTTP request failed! 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N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit |pdf=|usr=}}{{24786209}} gab.com Text N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=24786209 #FOAvip Background: Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. Objective: To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. Materials and methods: We selected notes from 2001?2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). Results: Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. Discussion: Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. Conclusions: SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. Twit Text FOAVip N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=24786209 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24786209 #FOAvip English Wikipedia <ref>{{Cite pmid|24786209}}</ref> N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit #MMPMID24786209 Marafino BJ; Davies JM; Bardach NS; Dean ML; Dudley RA; Boscardin J J Am Med Inform Assoc 2014[Sep]; 21 (5): 871-5 PMID24786209show ga Background: Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. Objective: To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. Materials and methods: We selected notes from 2001?2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). Results: Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. Discussion: Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. Conclusions: SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. äN-gram support vector machines for scalable procedure and diagnosis cl(epmc).pdf DeepDyve Pubget Overpricing