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Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records
Objective Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. Methods We conducted a retrospective study u...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788909/ https://www.ncbi.nlm.nih.gov/pubmed/36413995 http://dx.doi.org/10.1055/s-0042-1757880 |
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author | Patel, Jay Sureshbhai Brandon, Ryan Tellez, Marisol Albandar, Jasim M. Rao, Rishi Krois, Joachim Wu, Huanmei |
author_facet | Patel, Jay Sureshbhai Brandon, Ryan Tellez, Marisol Albandar, Jasim M. Rao, Rishi Krois, Joachim Wu, Huanmei |
author_sort | Patel, Jay Sureshbhai |
collection | PubMed |
description | Objective Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. Methods We conducted a retrospective study using EDR data of patients ( n = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021. We determined the completeness of patient demographics, periodontal charting, and PD diagnoses information in the EDR. Next, we developed two automated computer algorithms to automatically diagnose patients' PD statuses from clinical notes and periodontal charting data. Last, we phenotyped PD diagnoses using automated computer algorithms and reported the improved completeness of diagnosis. Results The completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% ( n = 9,834), diagnoses in clinical notes 18% ( n = 4,867), and charting information 80% ( n = 21,710). After phenotyping, the completeness of PD diagnoses improved to 100%. Eleven percent of patients had healthy periodontium, 43% were with gingivitis, 3% with stage I, 36% with stage II, and 7% with stage III/IV periodontitis. Conclusions We successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables. |
format | Online Article Text |
id | pubmed-9788909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-97889092022-12-24 Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records Patel, Jay Sureshbhai Brandon, Ryan Tellez, Marisol Albandar, Jasim M. Rao, Rishi Krois, Joachim Wu, Huanmei Methods Inf Med Objective Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. Methods We conducted a retrospective study using EDR data of patients ( n = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021. We determined the completeness of patient demographics, periodontal charting, and PD diagnoses information in the EDR. Next, we developed two automated computer algorithms to automatically diagnose patients' PD statuses from clinical notes and periodontal charting data. Last, we phenotyped PD diagnoses using automated computer algorithms and reported the improved completeness of diagnosis. Results The completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% ( n = 9,834), diagnoses in clinical notes 18% ( n = 4,867), and charting information 80% ( n = 21,710). After phenotyping, the completeness of PD diagnoses improved to 100%. Eleven percent of patients had healthy periodontium, 43% were with gingivitis, 3% with stage I, 36% with stage II, and 7% with stage III/IV periodontitis. Conclusions We successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables. Georg Thieme Verlag KG 2022-11-22 /pmc/articles/PMC9788909/ /pubmed/36413995 http://dx.doi.org/10.1055/s-0042-1757880 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Patel, Jay Sureshbhai Brandon, Ryan Tellez, Marisol Albandar, Jasim M. Rao, Rishi Krois, Joachim Wu, Huanmei Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records |
title | Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records |
title_full | Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records |
title_fullStr | Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records |
title_full_unstemmed | Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records |
title_short | Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records |
title_sort | developing automated computer algorithms to phenotype periodontal disease diagnoses in electronic dental records |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788909/ https://www.ncbi.nlm.nih.gov/pubmed/36413995 http://dx.doi.org/10.1055/s-0042-1757880 |
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