Integrative analysis of mortality risk in SFTS using machine learning and genetic approaches.

Helin Zha, Lianzi Wang, Haoyang Sun, Jie Li, Yajing Wang

Abstract read

In one paragraph

Article in mSphere, 2026. 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

5 authors.

Helin ZhaDepartment of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Lianzi WangDepartment of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Haoyang SunFaculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau.

Jie LiDepartment of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID 0009-0001-3795-1534

Yajing WangDepartment of Rehabilitation, Hefei Third People's Hospital, Hefei, Anhui, China.ORCID 0009-0008-3109-6152

Funding

Anhui Provincial Unversity Collaboration Major Project, Anhui Provincial Department of Eduication 2022 AH 040162

6 · The paper itself

Abstract

This study presents a novel approach to predicting mortality risk in patients with severe fever with thrombocytopenia syndrome (SFTS) by integrating machine learning models and genetic analysis techniques. Utilizing clinical data from 107 confirmed cases, we developed several predictive models and identified the extreme gradient boosting (XGBoost) algorithm as the most effective, achieving an area under the curve (AUC) of 0.950. Further analysis was performed based on the 15 robust features selected and performance-optimized through the Boruta algorithm, integrating

Indexed as

Machine LearningSevere Fever with Thrombocytopenia SyndromeBoosting Machine Learning AlgorithmsHumansMalePhlebovirusPredictive Learning Modelsextreme gradient boostingmachine learningMendelian randomizationmetabolic pathwaysmortality predictionsevere fever with thrombocytopenia syndrome

Identifiers

PMID42300796

DOI10.1128/msphere.00831-25

PMCPMC13410981

What OpenQuestion holds

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Registered trials

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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.