Evidence map›Paper›PMID 41044590›Full record

ArticleBMC psychiatry2025

Predicting sleep disturbance among patients with breast cancer in China through machine learning algorithms-a multi-site survey study.

Chong Liu, Shu Li, Xiaofang Zhao, Lingfang Ning, Cong Li, Xiaoshi Yang

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC psychiatry, 2025. 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

6 authors.

Chong Liu *Department of General Surgery, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110022, China.ORCID http://orcid.org/0009-0005-9610-7500
Shu LiDepartment of Health Economic, College of Health Management, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, China.ORCID http://orcid.org/0000-0002-1505-9908
Xiaofang Zhao *Department of Health Economic, College of Health Management, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, China.ORCID http://orcid.org/0009-0001-4142-5705
Lingfang NingDepartment of Health Economic, College of Health Management, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, China.ORCID http://orcid.org/0009-0008-2818-1989
Cong Li *Department of Health Law and Policy, College of Medical Humanities, China Medical University, Shenyang, Liaoning Province, 110122, P.R. China. licong850125@163.com.ORCID http://orcid.org/0009-0000-4264-6457
Xiaoshi YangDepartment of Health Economic, College of Health Management, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, China. xsyang@cmu.edu.cn.ORCID http://orcid.org/0000-0003-1426-128X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSleep disturbance is a common physical and mental disease, which has a high incidence in breast cancer population and has a great impact on patients treatment outcomes. This study aimed to construct a predictive model for sleep disturbance in breast cancer using machine learning (ML) algorithms and identify the most vital risk predictors.

methodsThrough cross-sectional stratified random sampling, 644 breast cancer patients completed a face-to-face interview questionnaire. Sleep disturbances were assessed using the Patient-Reported Outcomes Measurement Information System Sleep Disturbance 8-item short form. Based on the Maximum Relevant and Minimum Redundancy (MRMR) method, the importance of 26 predictive factors was ranked. Four ML algorithms were used to develop models that predict sleep disturbances in breast cancer.

resultsThe prevalence of sleep disturbance among breast cancer was 30.59%. The four ML models showed good prediction performances in the test set (area under the receiver operating characteristic curve: 0.74–0.83; accuracy: 0.73–0.82). The features strongly associated with sleep disturbance were loneliness, new possibilities of post-traumatic growth, anxiety, depression, and social support.

conclusionsML can effectively predict sleep disturbances in breast cancer. Interventions targeting on the reduction of loneliness, anxiety and depression and enhancement of post-traumatic growth (new possibilities) and social support are needed to alleviate sleep disturbances.

Indexed as

Breast NeoplasmsMachine LearningSleep Wake DisordersAdultAgedChinaClassification AlgorithmsCross-Sectional StudiesFemaleHumansMiddle AgedPrediction AlgorithmsPredictive Learning ModelsSurveys and QuestionnairesBreast cancerMachine learningPredictive modelSleep disturbance

Identifiers

PMID41044590
PMCPMC12495715

What OpenQuestion 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 OpenQuestion graph.