Evidence map›Paper›PMID 38542012›Full record

ArticleJournal of clinical medicine2024

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia.

Irfan Al-Hussaini, Brandon White, Armon Varmeziar, Nidhi Mehra, Milagro Sanchez, Judy Lee, Nicholas P DeGroote, Tamara P Miller, Cassie S Mitchell

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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

9 authors at 3 institutions in 1 country.

Irfan Al-HussainiLaboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0003-2969-7019
Brandon WhiteLaboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0002-8070-9778
Armon VarmeziarLaboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Nidhi MehraLaboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Milagro SanchezLaboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Judy LeeAflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA 30322, USA.
Nicholas P DeGrooteAflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA 30322, USA.
Tamara P MillerAflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA 30322, USA.
Cassie S MitchellLaboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0002-5472-6355
Georgia Institute of Technology · USAflac (United States) · USEmory University · US

Funding

Integrative predictive medicine to identify disease causes, develop cures, and optimize patient careR35GM152245 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI Cassie S Mitchell · 2024 to 2026
$1.1M
A Big Data Approach to BCR ABL LeukemiasR21CA232249 · NCI · GEORGIA INSTITUTE OF TECHNOLOGY · PI MITCHELL, CASSIE S · 2019 to 2020
$358k
NCI NIH HHS R21 CA232249NIGMS NIH HHS R35 GM152245NIH HHS R21CA232249
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

artificial intelligenceinfectionmachine learningnatural language processingpediatric leukemia

Identifiers

PMID38542012
PMCPMC10970787
OpenAlexW4392988140

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.