Evidence map›Paper›PMID 41214653›Full record

ArticleBMC medical informatics and decision making2025

Predicting the risk of preterm birth with machine learning and electronic health records in China.

Lushuai Qian, Hanyue Jia, Zhou Chang, Yanjun Hu, Chunling Chen, Xiaoqing Li, Hongping Zhang

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In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

7 authors.

Lushuai Qian *China Jiliang University, Hangzhou, Zhejiang, 310000, China.
Hanyue Jia *China Jiliang University, Hangzhou, Zhejiang, 310000, China.
Zhou ChangChina Jiliang University, Hangzhou, Zhejiang, 310000, China.
Yanjun HuWenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, No. 57 Canghou Street, Lucheng District, Wenzhou, Zhejiang, 325000, China.
Chunling ChenWenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, No. 57 Canghou Street, Lucheng District, Wenzhou, Zhejiang, 325000, China.
Xiaoqing LiWenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, No. 57 Canghou Street, Lucheng District, Wenzhou, Zhejiang, 325000, China. wzslixq@163.com.
Hongping ZhangWenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, No. 57 Canghou Street, Lucheng District, Wenzhou, Zhejiang, 325000, China. zjzhp@126.com.

Funding

the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China LBY23H200008the Medical Health Science and Technology Project of Zhejiang Provincial 2022KY1207the Medical Health Science and Technology Project of Zhejiang Provincial 2023RC272the Science and Technology Planning Project of Wenzhou Y2023088the Science and Technology Planning Project of Wenzhou ZY2021025
6 · The paper itself

Abstract

backgroundPreterm birth is a serious global public health issue, and early prediction in pregnant women is crucial for timely intervention and reduction of the incidence preterm births. We aimed to predict and validate the risk of preterm birth with machine learning, deep learning, and electronic health records in China. MATERIALS AND

methodsData were collected from 58,424 pregnant women between May 2015 and April 2024. After excluding incomplete records, a total of 36,378 cases were included, consisting of 34,132 full-term births and 2,246 preterm births. Of the 24 known high-risk factors for preterm birth, 20 statistically significant features were identified for model construction. Six machine learning algorithms were applied to process the data containing missing values, and 22 models were developed for predicting preterm births using the imputed data. Additionally, two dynamic deep learning methods were incorporated in our model development process.

resultsAmong the machine learning models, the Random Forest model performed best in both datasets with missing values and imputed data, achieving a maximum AUC of 0.826. The LightGBM model also exhibited strong performance, even with fewer features. Among the deep learning models, the LSTM model performed better, with an AUC of 0.851. Additionally, data from 10,367 pregnant women, collected between May and December 2024, were used as an external validation set, confirming the model’s stability.

conclusionsThe findings of this study indicate that both machine learning and deep learning models using electronic health records are valuable for preterm birth risk screening, supporting their use in clinical practice for preterm birth risk management. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Deep LearningElectronic Health RecordsMachine LearningPremature BirthAdultChinaClassification AlgorithmsFemaleHumansPrediction AlgorithmsPredictive Learning ModelsPregnancyRandom ForestRisk AssessmentRisk FactorsDeep learningElectronic health recordsMachine learningPredictionPreterm birth

Identifiers

PMID41214653
PMCPMC12604261

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.