BACKGROUND: The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. OBJECTIVE: To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. MATERIALS AND METHODS: We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. RESULTS: We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. CONCLUSIONS: This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed.
BACKGROUND: The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. OBJECTIVE: To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. MATERIALS AND METHODS: We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. RESULTS: We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. CONCLUSIONS: This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed.
Entities:
Keywords:
Decision Support Systems, Clinical; Electronic Health Records/standards*; Medical Informatics; Semantics*
Authors: Cui Tao; Guoqian Jiang; Thomas A Oniki; Robert R Freimuth; Qian Zhu; Deepak Sharma; Jyotishman Pathak; Stanley M Huff; Christopher G Chute Journal: J Am Med Inform Assoc Date: 2012-12-25 Impact factor: 4.497
Authors: 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 Journal: J Biomed Inform Date: 2012-02-04 Impact factor: 6.317
Authors: Jie Xu; Luke V Rasmussen; Pamela L Shaw; Guoqian Jiang; Richard C Kiefer; Huan Mo; Jennifer A Pacheco; Peter Speltz; Qian Zhu; Joshua C Denny; Jyotishman Pathak; William K Thompson; Enid Montague Journal: J Am Med Inform Assoc Date: 2015-07-29 Impact factor: 4.497
Authors: María del Carmen Legaz-García; Marcos Menárguez-Tortosa; Jesualdo Tomás Fernández-Breis; Christopher G Chute; Cui Tao Journal: J Am Med Inform Assoc Date: 2015-02-10 Impact factor: 4.497
Authors: José Alberto Maldonado; Mar Marcos; Jesualdo Tomás Fernández-Breis; Estíbaliz Parcero; Diego Boscá; María Del Carmen Legaz-García; Begoña Martínez-Salvador; Montserrat Robles Journal: AMIA Annu Symp Proc Date: 2017-02-10
Authors: Richard L Bradshaw; Kensaku Kawamoto; Kimberly A Kaphingst; Wendy K Kohlmann; Rachel Hess; Michael C Flynn; Claude J Nanjo; Phillip B Warner; Jianlin Shi; Keaton Morgan; Kadyn Kimball; Pallavi Ranade-Kharkar; Ophira Ginsburg; Melody Goodman; Rachelle Chambers; Devin Mann; Scott P Narus; Javier Gonzalez; Shane Loomis; Priscilla Chan; Rachel Monahan; Emerson P Borsato; David E Shields; Douglas K Martin; Cecilia M Kessler; Guilherme Del Fiol Journal: J Am Med Inform Assoc Date: 2022-04-13 Impact factor: 4.497
Authors: M R Santos; T Q V de Sá; F E da Silva; M R Dos Santos Junior; T A Maia; Z S N Reis Journal: Appl Clin Inform Date: 2017-12-14 Impact factor: 2.342
Authors: María Del Carmen Legaz-García; José Antonio Miñarro-Giménez; Marcos Menárguez-Tortosa; Jesualdo Tomás Fernández-Breis Journal: J Biomed Semantics Date: 2016-06-03