Deep Learning from EEG Reports for Inferring Underspecified Information.

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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

1. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018
   Pooled it
2. Active deep learning for the identification of concepts and relations in electroencephalography reports.Journal of biomedical informatics · 2019
   Article
3. Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019
   Article
4. Inferring Clinical Correlations from EEG Reports with Deep Neural Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017
   Article

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

Abstract