ArticleNPJ digital medicine2024
Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes.Sensors (Basel, Switzerland) · 2026Article
- Data Resource Profile: Cheeloo Lifespan Electronic-health reseArch Data-library (Cheeloo LEAD).International journal of epidemiology · 2026Article
- Closed loop text guided framework for lung cancer lesion segmentation and quantification.NPJ digital medicine · 2026Article
- Transforming Care in China by Using Public-Private Partnerships to Unlock the Power of Patient-Centered Real-World Data.Pharmaceutical medicine · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed an interpretable model, BOUND (Bayesian netwOrk for large-scale lUng caNcer Digital prescreening), using a comprehensive EHR dataset from the China to improve lung cancer detection rates. BOUND employs Bayesian network uncertainty inference, allowing it to predict lung cancer risk even with missing data and identify high-risk factors. Developed using data from 905,194 individuals, BOUND achieved an AUC of 0.866 in internal validation, with time- and geography-based external validations yielding AUCs of 0.848 and 0.841, respectively. In datasets with 10%-70% missing data, AUC ranged from 0.827 - 0.746. The model demonstrates strong calibration, clinical utility, and robust performance in both balanced and imbalanced datasets. A risk scorecard was also created, improving detection rates up to 6.8 times, available free online ( https://drzhang1.aiself.net/ ). BOUND enables non-radiative, cost-effective lung cancer prescreening, excels with missing data, and addresses treatment inequities in resource-limited primary healthcare settings.
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Registered trials
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.