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Amplifying Domain Expertise in Clinical Data Pipelines

Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert w...

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Detalles Bibliográficos
Autores principales: Rahman, Protiva, Nandi, Arnab, Hebert, Courtney
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677017/
https://www.ncbi.nlm.nih.gov/pubmed/33151150
http://dx.doi.org/10.2196/19612
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author Rahman, Protiva
Nandi, Arnab
Hebert, Courtney
author_facet Rahman, Protiva
Nandi, Arnab
Hebert, Courtney
author_sort Rahman, Protiva
collection PubMed
description Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.
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spelling pubmed-76770172020-11-23 Amplifying Domain Expertise in Clinical Data Pipelines Rahman, Protiva Nandi, Arnab Hebert, Courtney JMIR Med Inform Viewpoint Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study. JMIR Publications 2020-11-05 /pmc/articles/PMC7677017/ /pubmed/33151150 http://dx.doi.org/10.2196/19612 Text en ©Protiva Rahman, Arnab Nandi, Courtney Hebert. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.11.2020. 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 Viewpoint
Rahman, Protiva
Nandi, Arnab
Hebert, Courtney
Amplifying Domain Expertise in Clinical Data Pipelines
title Amplifying Domain Expertise in Clinical Data Pipelines
title_full Amplifying Domain Expertise in Clinical Data Pipelines
title_fullStr Amplifying Domain Expertise in Clinical Data Pipelines
title_full_unstemmed Amplifying Domain Expertise in Clinical Data Pipelines
title_short Amplifying Domain Expertise in Clinical Data Pipelines
title_sort amplifying domain expertise in clinical data pipelines
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677017/
https://www.ncbi.nlm.nih.gov/pubmed/33151150
http://dx.doi.org/10.2196/19612
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