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Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis

BACKGROUND: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record–based observational studies requires rich functionality to capture these temporal entities and relations. However,...

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Autores principales: Capurro, Daniel, Barbe, Mario, Daza, Claudio, Santa Maria, Josefa, Trincado, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723741/
https://www.ncbi.nlm.nih.gov/pubmed/33231554
http://dx.doi.org/10.2196/medinform.6924
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author Capurro, Daniel
Barbe, Mario
Daza, Claudio
Santa Maria, Josefa
Trincado, Javier
author_facet Capurro, Daniel
Barbe, Mario
Daza, Claudio
Santa Maria, Josefa
Trincado, Javier
author_sort Capurro, Daniel
collection PubMed
description BACKGROUND: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record–based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. OBJECTIVE: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. METHODS: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. RESULTS: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. CONCLUSIONS: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system.
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spelling pubmed-77237412020-12-11 Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis Capurro, Daniel Barbe, Mario Daza, Claudio Santa Maria, Josefa Trincado, Javier JMIR Med Inform Original Paper BACKGROUND: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record–based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. OBJECTIVE: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. METHODS: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. RESULTS: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. CONCLUSIONS: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system. JMIR Publications 2020-11-24 /pmc/articles/PMC7723741/ /pubmed/33231554 http://dx.doi.org/10.2196/medinform.6924 Text en ©Daniel Capurro, Mario Barbe, Claudio Daza, Josefa Santa Maria, Javier Trincado. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 24.11.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Capurro, Daniel
Barbe, Mario
Daza, Claudio
Santa Maria, Josefa
Trincado, Javier
Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis
title Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis
title_full Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis
title_fullStr Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis
title_full_unstemmed Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis
title_short Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis
title_sort temporal design patterns for digital phenotype cohort selection in critical care: systematic literature assessment and qualitative synthesis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723741/
https://www.ncbi.nlm.nih.gov/pubmed/33231554
http://dx.doi.org/10.2196/medinform.6924
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