ArticleNeurosurgical review2024
Adopting machine learning to predict ICU delirium.
Article in Neurosurgical review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing stroke prediction: a systematic review of AI-powered wearable technologies for early detection of stroke.Neurosurgical review · 2025Pooled it
- Artificial intelligence in brain surgery: Improving patient outcomes.Neurosurgical review · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With neuropsychiatric complications recognized among COVID-19 patients translating into significant morbidity, we explore the current state-of-the-art for auto Machine Learning (ML) to predict ICU delirium among severe COVID-19 patients which has been identified as a significant predictor of cognitive decline among such patients. Such optimally developed ML models can provide instantaneous, accurate and precise risk-stratification predictions, allowing neurology clinicians to take an informed decision regarding the advanced neuropsychiatric management for severe COVID-19 patients. Such incorporation of ML into the relevant management protocols has the potential to significantly curtail the morbidity and mortality associated with the once-in-a-century global public health catastrophe.
Indexed as
Identifiers
39060757What OpenQuestion holds
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.