Evidence map›Paper›PMID 39282083›Full record

ArticleJAMIA open2024

PheW

Jia Guo, Krzysztof Kiryluk, Shuang Wang

Abstract read
In one paragraph

Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jia GuoDepartment of Biostatistics, Columbia University, New York, NY 10032, United States.ORCID https://orcid.org/0000-0002-9774-9856
Krzysztof KirylukDepartment of Medicine, Columbia University, New York, NY 10032, United States.
Shuang WangDepartment of Biostatistics, Columbia University, New York, NY 10032, United States.ORCID https://orcid.org/0000-0002-1693-6888

Funding

Deep learning based predictive analytics to improve patient health.R01LM013061 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Krzysztof Kiryluk, CHUNHUA WENG · 2019 to 2026
$2.7M
NLM NIH HHS R01 LM013061
6 · The paper itself

Abstract

Objective: Electronic health records (EHRs) provide opportunities for the development of computable predictive tools. Conventional machine learning methods and deep learning methods have been widely used for this task, with the approach of usually designing one tool for one clinical outcome. Here we developed PheW Materials and Methods: PheW Results: Across 942 phenome-wide predictions using the MIMIC-III database, PheW Discussion: PheW Conclusions: PheW

Indexed as

electronic health records (EHRs)patient representationsphenome-wide prediction

Identifiers

PMID39282083
PMCPMC11401611

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.