Evidence map›Paper›PMID 42313416›Full record

ArticleInternational forum of allergy & rhinology2026

Machine Learning-Based Predictive Modeling for the Development of Chronic Rhinosinusitis Using Longitudinal Health Records.

Justina R Varghese, Sicong Chang, Akshay R Prabhakar, Jason F Zhang, Richard A Balbin, Sebastian Guadarrama-Sistos-Vazquez, Aatin Dhanda, Renjie Hu, Michael T Yim, Xin Fu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International forum of allergy & rhinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Justina R VargheseDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.ORCID https://orcid.org/0009-0001-8397-4155
Sicong ChangDepartment of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA.
Akshay R PrabhakarDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.
Jason F ZhangTexas A&M School of Engineering Medicine, Houston, Texas, USA.
Richard A BalbinTexas A&M School of Engineering Medicine, Houston, Texas, USA.
Sebastian Guadarrama-Sistos-VazquezDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.
Aatin DhandaDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-8926-7662
Renjie HuDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.
Michael T YimDepartment of Otolaryngology - Rhinology and Skull Base Surgery, LSU Health Shreveport, Shreveport, Louisiana, USA.ORCID https://orcid.org/0000-0002-8599-1637
Xin FuDepartment of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA.
Masayoshi TakashimaDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.ORCID https://orcid.org/0000-0001-9790-3529
Omar G AhmedDepartment of Otolaryngology - Head and Neck Surgery, Houston Methodist Hospital, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

key pointsMachine learning identifies reproducible pre-diagnostic CRS trajectories in large EHR cohorts. Models show stable external performance (AUC ∼ 0.81), supporting real-world generalizability. Predictors highlight evolving CRS workups and models may serve as future decision support tools.

Indexed as

Machine LearningRhinosinusitisChronic DiseaseElectronic Health RecordsHumansPrediction AlgorithmsPredictive Learning Modelschronic rhinosinusitismachine learningpredictive modeling

Identifiers

PMID42313416
PMCPMC13432442

What OpenQuestion holds

Textmetadata
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.