SynthesisFrontiers in cardiovascular medicine2022
Machine learning for predicting acute hypotension: A systematic review.
Synthesis in Frontiers in cardiovascular medicine, 2022. 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, 8 citations in OpenAlex.
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An acute hypotensive episode (AHE) can lead to severe consequences and complications that threaten patients' lives within a short period of time. How to accurately and non-invasively predict AHE in advance has become a hot clinical topic that has attracted a lot of attention in the medical and engineering communities. In the last 20 years, with rapid advancements in machine learning methodology, this topic has been viewed from a different perspective. This review paper examines studies published from 2008 to 2021 that evaluated the performance of various machine learning algorithms developed to predict AHE. A total of 437 articles were found in four databases that were searched, and 35 full-text articles were included in this review. Fourteen machine learning algorithms were assessed in these 35 articles; the Support Vector Machine algorithm was studied in 12 articles, followed by Logistic Regression (six articles) and Artificial Neural Network (six articles). The accuracy of the algorithms ranged from 70 to 96%. The size of the study sample varied from small (12 subjects) to very large (3,825 subjects). Recommendations for future work are also discussed in this review.
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Identifiers
What 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.