Evidence map›Paper›PMID 38350899›Full record

ArticleBMC medical informatics and decision making2024

Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency.

Qianning Liu, Yifan Chen, Peng Xie, Ying Luo, Buxuan Wang, Yuanxi Meng, Jiaqian Zhong, Jiaqi Mei, Wei Zou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2024. 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
1.5field-weighted citation impact, top 18% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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.

Qianning Liu *School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China.
Yifan Chen *School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China.
Peng Xie *Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Ying Luo *Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Buxuan WangSchool of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China.
Yuanxi MengThe First Clinical Medical College,Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Jiaqian ZhongThe First Clinical Medical College,Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Jiaqi MeiThe First Clinical Medical College,Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Wei ZouDepartment of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China. ieeeif@hotmail.com.
First Affiliated Hospital of Jiangxi Medical College · CNJiangxi University of Finance and Economics · CNNanchang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSecondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown.

methodsMedical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection.

resultsSeveral metrics were used to evaluate the models' performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively.

conclusionsThe models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.

Indexed as

HIV InfectionsBenchmarkingChinaHospitalsHumansMachine LearningBoosted logistic regressionGradient boosting machineImbalanced dataK nearest neighbourPathogensRandom forestSecondary immunodeficiency

Identifiers

PMID38350899
PMCPMC10863296
OpenAlexW4391778862

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.