دورية أكاديمية
Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal.
العنوان: | Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal. |
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المؤلفون: | Vagliano I; Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Amsterdam, the Netherlands. Electronic address: i.vagliano@amsterdamumc.nl., Dormosh N; Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Amsterdam, the Netherlands., Rios M; Centre for Translation Studies, University of Vienna, Vienna, Austria. Electronic address: https://twitter.com/zhizhid., Luik TT; Amsterdam Public Health (APH), Amsterdam, the Netherlands; Dept. of Medical Biology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands., Buonocore TM; Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy., Elbers PWG; Amsterdam Public Health (APH), Amsterdam, the Netherlands; Dept. of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. Electronic address: https://twitter.com/zhizhid., Dongelmans DA; Amsterdam Public Health (APH), Amsterdam, the Netherlands; National Intensive Care Evaluation (NICE) Foundation, Amsterdam, the Netherlands; Dept. of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands., Schut MC; Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Amsterdam, the Netherlands; Dept. of Clinical Chemistry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands., Abu-Hanna A; Dept. of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health (APH), Amsterdam, the Netherlands. |
المصدر: | Journal of biomedical informatics [J Biomed Inform] 2023 Oct; Vol. 146, pp. 104504. Date of Electronic Publication: 2023 Sep 22. |
نوع المنشور: | Journal Article; Review |
اللغة: | English |
بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE |
أسماء مطبوعة: | Publication: Orlando : Elsevier Original Publication: San Diego, CA : Academic Press, c2001- |
مستخلص: | Objective: To review and critically appraise published and preprint reports of prognostic models of in-hospital mortality of patients in the intensive-care unit (ICU) based on neural representations (embeddings) of clinical notes. Methods: PubMed and arXiv were searched up to August 1, 2022. At least two reviewers independently selected the studies that developed a prognostic model of in-hospital mortality of intensive-care patients using free-text represented as embeddings and extracted data using the CHARMS checklist. Risk of bias was assessed using PROBAST. Reporting on the model was assessed with the TRIPOD guideline. To assess the machine learning components that were used in the models, we present a new descriptive framework based on different techniques to represent text and provide predictions from text. The study protocol was registered in the PROSPERO database (CRD42022354602). Results: Eighteen studies out of 2,825 were included. All studies used the publicly-available MIMIC dataset. Context-independent word embeddings are widely used. Model discrimination was provided by all studies (AUROC 0.75-0.96), but measures of calibration were scarce. Seven studies used both structural clinical variables and notes. Model discrimination improved when adding clinical notes to variables. None of the models was externally validated and often a simple train/test split was used for internal validation. Our critical appraisal demonstrated a high risk of bias in all studies and concerns regarding their applicability in clinical practice. Conclusion: All studies used a neural architecture for prediction and were based on one publicly available dataset. Clinical notes were reported to improve predictive performance when used in addition to only clinical variables. Most studies had methodological, reporting, and applicability issues. We recommend reporting both model discrimination and calibration, using additional data sources, and using more robust evaluation strategies, including prospective and external validation. Finally, sharing data and code is encouraged to improve study reproducibility. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.) |
فهرسة مساهمة: | Keywords: Intensive care; Machine learning; Mortality; Natural language processing; Prognostic models; Systematic review |
تواريخ الأحداث: | Date Created: 20230924 Latest Revision: 20231012 |
رمز التحديث: | 20231215 |
DOI: | 10.1016/j.jbi.2023.104504 |
PMID: | 37742782 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1532-0480 |
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DOI: | 10.1016/j.jbi.2023.104504 |