Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review Article Swipe
Melissa Y. Yan
,
Lise Tuset Gustad
,
Øystein Nytrø
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocab236
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocab236
Objective To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. Materials and methods PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. Results The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. Discussion Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. Conclusions Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.
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Concepts
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocab236
- OA Status
- hybrid
- Cited By
- 96
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200334578Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamia/ocab236Digital Object Identifier
- Title
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Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic reviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-10-11Full publication date if available
- Authors
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Melissa Y. Yan, Lise Tuset Gustad, Øystein NytrøList of authors in order
- Landing page
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https://doi.org/10.1093/jamia/ocab236Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1093/jamia/ocab236Direct OA link when available
- Concepts
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Sepsis, Identification (biology), Receiver operating characteristic, Artificial intelligence, Medicine, Machine learning, Computer science, Septic shock, Natural language processing, Unstructured data, Intensive care medicine, Data science, Data mining, Big data, Internal medicine, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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96Total citation count in OpenAlex
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2025: 39, 2024: 25, 2023: 26, 2022: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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