ArticleiScience2025
Prediction of ICU needs and organ failures in infectious patients using machine learning.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Early prediction of severe autoimmune encephalitis: development and validation of a model incorporating readily available lactate dehydrogenase.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This retrospective multicenter cohort study developed predictive models to identify patients at high risk for intensive care and major organ failure, using data from the First Affiliated Hospital of Xiamen University and the MIMIC-III/IV databases. A total of 18,689 infectious patients were analyzed. Nine machine learning algorithms were employed, with CatBoost demonstrating the best performance. The intensive care model achieved an area under the receiver operating characteristic curve (AUC) of 0.956, while organ failure prediction models for renal, heart, respiratory, hepatic, and coagulation systems showed strong accuracy, with most models achieving AUCs over 0.8. Random Forest performed best for predicting respiratory and renal failures, with AUCs of 0.677 and 0.836, respectively. Model interpretability was supported by real clinical cases, where abnormal indicators aligned with key predictive features. These models offer valuable insights for early decision-making, enhancing resource allocation and patient outcomes.
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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 Socratic graph.