ArticleNPJ digital medicine2025
Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors.
Article in NPJ digital medicine, 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
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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
1 citing paper in PubMed.
- Predicting dynamic individual out-of-hospital cardiac arrest risks using explainable machine learning: a multicenter study in China.NPJ digital medicine · 2026Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of an accurate system to predict the daily incidence of out-of-hospital cardiac arrest (OHCA) might provide a significant public health benefit. We developed a machine learning model to predict daily OHCA incidence at the regional level using high-resolution meteorological data, chronological data, and sociodemographic data. Among OHCAs of non-traumatic cause, 196,735 in the training dataset (2013-2017), 119,455 in the testing dataset (2018-2019) from internal areas, and 37,160 in the testing dataset from external areas were included in the analysis. Application of invariant causal prediction (ICP) successfully reduced the number of variables to 17 while maintaining high predictive performance for nationwide incidence per 100,000 per day in both the training and testing datasets, whether from internal or external regions, comparable to the non-ICP model using the initial 34 variables. Furthermore, the prediction model retained a satisfactory performance up to seven days in advance.
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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.