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Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review


Sedlakova, Jana; Daniore, Paola; Horn Wintsch, Andrea; Wolf, Markus; Stanikic, Mina; Haag, Christina; Sieber, Chloé; Schneider, Gerold; Staub, Kaspar; Ettlin, Dominik Alois; Grübner, Oliver; Rinaldi, Fabio; von Wyl, Viktor (2022). Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review. medRxiv 22278137, Cold Spring Harbor Laboratory.

Abstract

Digital data play an increasingly important role in advancing medical research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Specifically, unstructured data are available in a non-standardized format and require substantial preprocessing and feature extraction to translate them to meaningful insights. This might hinder their potential to advance health research, prevention, and patient care delivery, as these processes are resource intensive and connected with unresolved challenges. These challenges might prevent enrichment of structured evidence bases with relevant unstructured data, which we refer to as digital unstructured data enrichment. While prevalent challenges associated with unstructured data in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with existing data sources is missing.In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health along with possible solutions to address these challenges. Building on these findings, we compiled a checklist following the standard data flow in a research study to contribute to the limited available systematic guidance on digital unstructured data enrichment. This proposed checklist offers support in early planning and feasibility assessments for health research combining unstructured data with existing data sources. Finally, the sparsity and heterogeneity of unstructured data enrichment methods in our review call for a more systematic reporting of such methods to achieve greater reproducibility.

Abstract

Digital data play an increasingly important role in advancing medical research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Specifically, unstructured data are available in a non-standardized format and require substantial preprocessing and feature extraction to translate them to meaningful insights. This might hinder their potential to advance health research, prevention, and patient care delivery, as these processes are resource intensive and connected with unresolved challenges. These challenges might prevent enrichment of structured evidence bases with relevant unstructured data, which we refer to as digital unstructured data enrichment. While prevalent challenges associated with unstructured data in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with existing data sources is missing.In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health along with possible solutions to address these challenges. Building on these findings, we compiled a checklist following the standard data flow in a research study to contribute to the limited available systematic guidance on digital unstructured data enrichment. This proposed checklist offers support in early planning and feasibility assessments for health research combining unstructured data with existing data sources. Finally, the sparsity and heterogeneity of unstructured data enrichment methods in our review call for a more systematic reporting of such methods to achieve greater reproducibility.

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Item Type:Working Paper
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
06 Faculty of Arts > Institute of Computational Linguistics
07 Faculty of Science > Institute of Geography
04 Faculty of Medicine > Institute of Biomedical Ethics and History of Medicine
04 Faculty of Medicine > Institute of Evolutionary Medicine
08 Research Priority Programs > Digital Society Initiative
06 Faculty of Arts > Zurich Center for Linguistics
06 Faculty of Arts > Linguistic Research Infrastructure (LiRI)
06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2022
Deposited On:09 Aug 2022 12:55
Last Modified:13 Mar 2024 14:51
Series Name:medRxiv
ISSN:0959-535X
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1101/2022.07.28.22278137
Related URLs:https://www.zora.uzh.ch/id/eprint/238391/
https://doi.org/10.1371/journal.pdig.0000347
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)