Evidence map›Paper›PMID 41199278›Full record

ArticleBMC medical informatics and decision making2025

From dry eye to depression: a machine learning-based framework for predicting adolescent mental health.

Le Han, Ying Liu, Peng Xian, Xiao Liu, Kai Cao, Li Ren, Yue Chang, Zhangfang Ma, Lei Tian, Shijing Deng and 3 more

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PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

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0citing papers in PubMed
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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

13 authors.

Le HanBeijing Tongren Hospital, Capital Medical University Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
Ying LiuBeijing Tongren Hospital, Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
Peng XianDepartment of Hospital Infection Control and Disease Prevention, Beijing Tongren Hospital, Beijing, China.
Xiao LiuBeijing Tongren Hospital, Capital Medical University Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
Kai CaoBeijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Li RenBeijing Tongren Hospital, Capital Medical University Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
Yue ChangBeijing Tongren Hospital, Capital Medical University Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
Zhangfang MaBeijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Lei TianBeijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Shijing DengBeijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Xuejiao LiuBeijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Yunshuang LiuBeijing Tongren Hospital, Capital Medical University Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
Ying JieBeijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China. yingcn@aliyun.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdolescent depression is a major public health concern. Physical health indicators are rarely included in risk tools. We examined whether adding dry eye disease (DED) to psychosocial and behavioral factors improves prediction of depressive symptoms.

methodsWe analyzed 2,076 adolescent questionnaires (94.5% response) covering ocular health, sleep, electronic device use, social support, and demographics. Five machine-learning classifiers were trained with cross-validation and evaluated for discrimination and calibration.

resultsModels that included DED achieved strong discrimination (AUC ≈ 0.84) and good calibration, with highest accuracy for no and severe depression and lower performance for mild/moderate categories.

conclusionsIntegrating ocular health with psychosocial factors enhances machine-learning prediction of adolescent depression and may support earlier, school-based identification and referral. Given the low-cost, questionnaire-based inputs and favorable calibration, this approach shows promise for population screening and targeted prevention, pending external validation and prospective testing.

Indexed as

DepressionDry Eye SyndromesMachine LearningMental HealthAdolescentFemaleHumansMaleSurveys and QuestionnairesAdolescent depressionDry eye diseaseElectronic device useMachine learningPredictive modelingPublic health screeningSleep qualitySocial support

Identifiers

PMID41199278
PMCPMC12593886

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LicenceCC BY-NC-ND
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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.