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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,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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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. |
format | Online Article Text |
id | pubmed-7723741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
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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