Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review.
Autor: | Moazemi S; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Vahdati S; Institute for Applied Informatics (InfAI), Dresden, Germany., Li J; Institute for Applied Informatics (InfAI), Dresden, Germany., Kalkhoff S; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Castano LJV; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Dewitz B; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Bibo R; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Sabouniaghdam P; Department of Computer Science, Heinrich-Hertz-Europakolleg, Bonn, Germany., Tootooni MS; Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL, United States., Bundschuh RA; Nuclear Medicine, Medical Faculty, University Augsburg, Augsburg, Germany., Lichtenberg A; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Aubin H; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany., Schmid F; Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany. |
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Jazyk: | angličtina |
Zdroj: | Frontiers in medicine [Front Med (Lausanne)] 2023 Mar 31; Vol. 10, pp. 1109411. Date of Electronic Publication: 2023 Mar 31 (Print Publication: 2023). |
DOI: | 10.3389/fmed.2023.1109411 |
Abstrakt: | Background: Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods: Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results: More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion: Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Moazemi, Vahdati, Li, Kalkhoff, Castano, Dewitz, Bibo, Sabouniaghdam, Tootooni, Bundschuh, Lichtenberg, Aubin and Schmid.) |
Databáze: | MEDLINE |
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