Evidence map›Paper›PMID 41856921›Full record

ArticleTropical medicine & international health : TM & IH2026

Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning.

Liliane Moreira Nery, Nícholas de Paula Nicomedes, Pedro Cesar Madureira de Godoy Camargo, Sidney Alves de Outeiro, Leopoldo André Dutra Lusquino Filho, Claudio Miceli de Farias, Darllan Collins da Cunha E Silva

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Tropical medicine & international health : TM & IH, 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

7 authors.

Liliane Moreira NerySão Paulo State University (UNESP), Institute of Science and Technology, Sorocaba, São Paulo State, Brazil.ORCID https://orcid.org/0000-0002-5352-5316
Nícholas de Paula NicomedesSão Paulo State University (UNESP), Institute of Science and Technology, Sorocaba, São Paulo State, Brazil.ORCID https://orcid.org/0009-0007-5941-1575
Pedro Cesar Madureira de Godoy CamargoSão Paulo State University (UNESP), Institute of Science and Technology, Sorocaba, São Paulo State, Brazil.ORCID https://orcid.org/0009-0004-0757-3757
Sidney Alves de OuteiroFederal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Rio de Janeiro State, Brazil.ORCID https://orcid.org/0009-0007-3141-1063
Leopoldo André Dutra Lusquino FilhoSão Paulo State University (UNESP), Institute of Science and Technology, Sorocaba, São Paulo State, Brazil.ORCID https://orcid.org/0000-0002-8283-3764
Claudio Miceli de FariasFederal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Rio de Janeiro State, Brazil.ORCID https://orcid.org/0000-0002-1927-7398
Darllan Collins da Cunha E SilvaSão Paulo State University (UNESP), Institute of Science and Technology, Sorocaba, São Paulo State, Brazil.ORCID https://orcid.org/0000-0003-3280-0478

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 444734/2023-6Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 001
6 · The paper itself

Abstract

Respiratory diseases remain a challenge in Brazil due to socioeconomic inequalities and environmental risks that intensify population vulnerability. This study compared XGBoost with a deep learning model using stacked Gated Recurrent Units (GRU), trained with morbidity data from respiratory diseases and exogenous variables such as per capita GDP, population density, urbanisation index and greenhouse gas emissions (1999-2023). These data were normalised and temporally disaggregated using synthetic data to refine time-series granularity. Results showed regional heterogeneity: the GRU achieved superior performance in Porto Alegre (R

Indexed as

Deep LearningRespiratory Tract DiseasesBoosting Machine Learning AlgorithmsBrazilForecastingHumansMorbidityPredictive Learning ModelsRecurrent Neural NetworksSocioeconomic Factorsepidemiological modellingpublic healthrecurrent neural networkssocioeconomic determinantstime series forecasting

Identifiers

PMID41856921
PMCPMC13246585

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.