Exploiting temporal relationships in the prediction of mortality.

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.

1. XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer.Journal of pathology informatics · 2023
   Article

Corrections and comments

• Comment on
  Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.2020

Authors and funding

2 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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