Literature DB >> 32308874

Clinical Tractor: A Framework for Automatic Natural Language Understanding of Clinical Practice Guidelines.

Daniel R Schlegel1, Kate Gordon1, Carmelo Gaudioso2, Mor Peleg3.   

Abstract

Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious and burdensome task, even using CIG tools and languages designed to improve the process. Natural language understanding (NLU) systems perform automated reading comprehension, parsing text and using reasoning to convert syntactic information from unstructured text into semantic information. Influenced by successful systems used in other domains, we present the architecture for a system which uses NLU approaches to create semantic representations of entire CPGs. In the future, these representations may be used to generate CIGs. ©2019 AMIA - All rights reserved.

Year:  2020        PMID: 32308874      PMCID: PMC7153137     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  11 in total

1.  Comprehensive categorization of guideline recommendations: creating an action palette for implementers.

Authors:  Abdelwaheb Essaihi; George Michel; Richard N Shiffman
Journal:  AMIA Annu Symp Proc       Date:  2003

2.  Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

Authors:  Guergana K Savova; James J Masanz; Philip V Ogren; Jiaping Zheng; Sunghwan Sohn; Karin C Kipper-Schuler; Christopher G Chute
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

3.  A practical method for transforming free-text eligibility criteria into computable criteria.

Authors:  Samson W Tu; Mor Peleg; Simona Carini; Michael Bobak; Jessica Ross; Daniel Rubin; Ida Sim
Journal:  J Biomed Inform       Date:  2010-09-17       Impact factor: 6.317

Review 4.  Computer-interpretable clinical guidelines: a methodological review.

Authors:  Mor Peleg
Journal:  J Biomed Inform       Date:  2013-06-25       Impact factor: 6.317

5.  Comment on American Diabetes Association. Standards of Medical Care in Diabetes-2017. Diabetes Care 2017;40(Suppl. 1):S1-S135.

Authors:  David P Sonne; Bianca Hemmingsen
Journal:  Diabetes Care       Date:  2017-07       Impact factor: 19.112

6.  Extraction and use of linguistic patterns for modelling medical guidelines.

Authors:  Radu Serban; Annette ten Teije; Frank van Harmelen; Mar Marcos; Cristina Polo-Conde
Journal:  Artif Intell Med       Date:  2006-09-11       Impact factor: 5.326

7.  Exploiting thesauri knowledge in medical guideline formalization.

Authors:  R Serban; Annete ten Teije
Journal:  Methods Inf Med       Date:  2009-05-15       Impact factor: 2.176

Review 8.  Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis.

Authors:  Lorenzo Moja; Koren H Kwag; Theodore Lytras; Lorenzo Bertizzolo; Linn Brandt; Valentina Pecoraro; Giulio Rigon; Alberto Vaona; Francesca Ruggiero; Massimo Mangia; Alfonso Iorio; Ilkka Kunnamo; Stefanos Bonovas
Journal:  Am J Public Health       Date:  2014-10-16       Impact factor: 9.308

9.  Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

Authors:  Qing T Zeng; Sergey Goryachev; Scott Weiss; Margarita Sordo; Shawn N Murphy; Ross Lazarus
Journal:  BMC Med Inform Decis Mak       Date:  2006-07-26       Impact factor: 2.796

10.  CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.

Authors:  Ergin Soysal; Jingqi Wang; Min Jiang; Yonghui Wu; Serguei Pakhomov; Hongfang Liu; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2018-03-01       Impact factor: 4.497

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.