An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain

This review examines how a highly structured data collection system could be used to create data-driven diagnostic classification algorithms. Some preliminary data using this process is provided. The data collection system described is applicable to any clinical domain where the diagnoses being expl...

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Autores principales:	Anette Paulina Vistoso Monreal, Nicolas Veas, Glenn Clark
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Orofacial pain Prediction Algorithms Modeling Machine learning Dentistry RK1-715
Acceso en línea:	https://doaj.org/article/06ac039ecbd04abab31b2dbd550bfd26
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spelling	oai:doaj.org-article:06ac039ecbd04abab31b2dbd550bfd262021-11-22T04:20:09ZAn artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain1882-761610.1016/j.jdsr.2021.11.001https://doaj.org/article/06ac039ecbd04abab31b2dbd550bfd262021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1882761621000314https://doaj.org/toc/1882-7616This review examines how a highly structured data collection system could be used to create data-driven diagnostic classification algorithms. Some preliminary data using this process is provided. The data collection system described is applicable to any clinical domain where the diagnoses being explored are based predominately on clinical history (subjective) and physical examination (objective) information. The system has been piloted and refined using patient encounters collected in a clinic specializing in Orofacial Pain treatment. In summary, whether you believe a branching hybrid check-box based data collection system with built-in algorithms is needed, depends on your individual agenda. If you have no plans for data analysis or publishing about the various phenotypes discovered and you do not need pop-up suggestions for best diagnosis and treatment options, it is easier to use a semi-structured narrative note for your patient encounters. If, however, you want data-driven diagnostic and disease risk algorithms and pop-up best-treatment options, then you need a highly structured data collection system that is compatible with machine learning analysis. Automating the journey from data collection to diagnoses has the potential to improve standards of care by providing faster and reliable predictions.Anette Paulina Vistoso MonrealNicolas VeasGlenn ClarkElsevierarticleOrofacial painPredictionAlgorithmsModelingMachine learningDentistryRK1-715ENJapanese Dental Science Review, Vol 57, Iss , Pp 242-249 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Orofacial pain Prediction Algorithms Modeling Machine learning Dentistry RK1-715
spellingShingle	Orofacial pain Prediction Algorithms Modeling Machine learning Dentistry RK1-715 Anette Paulina Vistoso Monreal Nicolas Veas Glenn Clark An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
description	This review examines how a highly structured data collection system could be used to create data-driven diagnostic classification algorithms. Some preliminary data using this process is provided. The data collection system described is applicable to any clinical domain where the diagnoses being explored are based predominately on clinical history (subjective) and physical examination (objective) information. The system has been piloted and refined using patient encounters collected in a clinic specializing in Orofacial Pain treatment. In summary, whether you believe a branching hybrid check-box based data collection system with built-in algorithms is needed, depends on your individual agenda. If you have no plans for data analysis or publishing about the various phenotypes discovered and you do not need pop-up suggestions for best diagnosis and treatment options, it is easier to use a semi-structured narrative note for your patient encounters. If, however, you want data-driven diagnostic and disease risk algorithms and pop-up best-treatment options, then you need a highly structured data collection system that is compatible with machine learning analysis. Automating the journey from data collection to diagnoses has the potential to improve standards of care by providing faster and reliable predictions.
format	article
author	Anette Paulina Vistoso Monreal Nicolas Veas Glenn Clark
author_facet	Anette Paulina Vistoso Monreal Nicolas Veas Glenn Clark
author_sort	Anette Paulina Vistoso Monreal
title	An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
title_short	An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
title_full	An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
title_fullStr	An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
title_full_unstemmed	An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
title_sort	artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/06ac039ecbd04abab31b2dbd550bfd26
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An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain

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