ArticleHealth science reports2026
Artificial Intelligence Prognostic Models for Healthcare-Associated Infection Prediction in Adult Intensive Care Units: A Systematic Review Protocol.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Background and Aims: Despite widespread Infection Prevention and Control (IPC) measures, Healthcare-Associated Infections (HAIs) remain a major concern in Intensive Care Units (ICUs), leading to increased mortality, prolonged hospital stays, and additional healthcare costs. Prognostic prediction models support identification of patients at high infection risk, and Machine Learning and Artificial Intelligence (ML/AI) methods offer the potential to improve such predictions. However, no systematic review has specifically examined ML/AI-based prognostic models for HAI prediction in adult ICU populations. This review aims to identify ML/AI-based prognostic models for estimating the HAI risk in adult ICU-patients, investigate their methodological characteristics, risk of bias and reporting quality. Methods: A systematic search will be conducted in PubMed, Scopus, and Web of Science to identify studies developing and/or validating ML/AI-based prognostic models of HAI prediction in adult ICU-admitted patients. Any study developing or evaluating supervised ML/AI-based models for the prediction of any HAI occurring after ICU admission will be included. Three reviewers will independently screen articles, extract data, and assess risk of bias using the Risk Of Bias ASsessment Tool for prediction models using Artificial Intelligence (PROBAST + AI). Extracted information will include study characteristics, patients and setting, predictors, sample size, missing data, analysis methods, performance, and model presentation and interpretation. A narrative synthesis will be conducted for all included studies. Finally, the SPIN-Prediction Models (SPIN-PM) framework will be used to assess quality of reporting. This systematic review protocol was registered on the Open Science Framework (osf. io/bzqfp). Conclusion: This systematic review's findings on methodological aspects, risk of bias, and applicability of ML/AI-based prognostic models for HAI prediction in adult ICU patients will help inform researchers, clinicians, and IPC decision-makers about these models' reliability and readiness for clinical use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.