{
  "abstract": "Objectives Lung cancer is the leading cause of cancer-related mortality worldwide, with poor prognosis largely due to late-stage diagnosis. Current screening methods such as low-dose CT face accessibility and cost barriers in resource-limited settings. This study develops a lightweight multichannel convolutional neural network for lung cancer screening support through longitudinal risk stratification using routine pre-diagnostic healthcare data.Methods We conducted a retrospective cohort study using Taiwan’s National Health Insurance Research Database, comprising 99 615 individuals (575 lung cancer cases; 99 040 non-cancer controls). Diagnostic codes, medication records and medical orders within a 36-month observation window were extracted. Log-likelihood ratio feature selection was implemented to reduce dimensionality, achieving 99.8% reduction in computational requirements while retaining clinical relevance. A multichannel Convolutional Neural Network (CNN) architecture was designed to process these heterogeneous data modalities simultaneously.Results The proposed method achieved an F₁-score of 0.5738, precision of 0.7149, Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8316 and Area Under the Precision-Recall Curve (AUPRC) of 0.1617, outperforming baseline methods in precision and F₁-score. Ablation studies confirmed that medical orders provide primary predictive value, while medication features contribute limited discriminative signal in the pre-diagnostic phase. SHapley Additive exPlanations analysis revealed that routine healthcare utilisation patterns, rather than cancer-specific features, drive risk stratification.Discussion The lightweight architecture enables deployment in resource-constrained clinical environments while maintaining robust performance, offering potential as a preliminary screening tool to identify high-risk individuals for further diagnostic examination.Conclusion Efficient deep learning models using routine clinical data can facilitate lung cancer risk stratification and screening, providing a scalable solution for clinical implementation.",
  "authors": [
    {
      "affiliations": [
        "Department of Computer Science, Middlesex University, London, UK",
        "International Center for Health Information Technology, Taipei Medical University, Taipei City, Taiwan",
        "Graduate Institute of Biomedical Informatics, College of Medical Science & Technology, Taipei Medical University, Taipei City, Taiwan"
      ],
      "name": "Chia-Hui Chien"
    },
    {
      "affiliations": [
        "Graduate Institute of Data Science, Taipei Medical University, Taipei City, Taiwan"
      ],
      "name": "Shih-Chuan Chang"
    },
    {
      "affiliations": [
        "Graduate Institute of Data Science, Taipei Medical University, Taipei City, Taiwan",
        "Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City, Taiwan"
      ],
      "name": "Yung-Chun Chang"
    },
    {
      "affiliations": [
        "International Center for Health Information Technology, Taipei Medical University, Taipei City, Taiwan",
        "Graduate Institute of Biomedical Informatics, College of Medical Science & Technology, Taipei Medical University, Taipei City, Taiwan",
        "Department of Dermatology, Wan Fang Hospital, Taipei City, Taiwan",
        "Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City, Taiwan"
      ],
      "name": "Yu-Chuan Li"
    }
  ],
  "title": "Longitudinal multisource clinical model for early lung cancer risk stratification and screening",
  "uid": "ef7a8644-c6c3-56bb-a7e9-dc91b8cbcba8"
}