Abstract
The impact of social determinants on individual health has increasingly been recognized. In this study, we aimed to understand the representation of stressful life events occurring in clinical reports generated outside of mental health specialties. We present a conceptual schema for the representation of stressful events and associated linguistic attributes within free-text clinical notes. We also evaluated existing 'off-the-shelf' clinical Natural Language Processing (NLP) systems for the detection of stress related concepts in clinical notes. We found that mentions of stressful events are prevalent even in non-mental health specialties, and that capturing details of stress mentions is challenging. Our results further indicate that existing NLP systems can serve as a reasonable starting point for developing models trained specifically for extracting stress associated information from clinical narratives.
Original language | English (US) |
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Title of host publication | Proceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 153-160 |
Number of pages | 8 |
ISBN (Electronic) | 9781665401326 |
DOIs | |
State | Published - Aug 2021 |
Event | 9th IEEE International Conference on Healthcare Informatics, ISCHI 2021 - Virtual, Victoria, Canada Duration: Aug 9 2021 → Aug 12 2021 |
Publication series
Name | Proceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021 |
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Conference
Conference | 9th IEEE International Conference on Healthcare Informatics, ISCHI 2021 |
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Country/Territory | Canada |
City | Virtual, Victoria |
Period | 8/9/21 → 8/12/21 |
Bibliographical note
Funding Information:ACKNOWLEDGEMENT This work was funded in part by the National Institute of Health’s National Center for Advancing Translation Science U01TR002062 and the Agency for Healthcare Research and Quality R01HS024532.
Publisher Copyright:
© 2021 IEEE.
Keywords
- Informatics
- Knowledge representation
- Medical information systems
- Text mining