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<p>Pneumoconiosis is a serious occupational disease with high morbidity and disability rates. However, the evolution of pneumoconiosis is complex and changeable, and the course of most cases is incomplete, resulting in a lack of continuity in a large number of data samples, which makes it challenging for radiologists to accurately assess the development of the disease. We propose a conditional self-attention TimesNet as the backbone network for time series analysis tasks, aiming to improve prognosis prediction based on disease progression information in pneumoconiosis image data at different time periods. In our approach, we train the model using chest X-ray images of the same patient at different time points, incorporating a hierarchical attention structure and self-attention blocks to fully consider the contextual correlation information of consecutive time-point images. Additionally, diverse clinical features of patients are utilized as conditional inputs in disease progression prediction. The goal is to better learn the progression status of the disease and reflect the disease trajectory representation of missing time series data, enhancing the model’s predictive capabilities. Simultaneously, an adversarial diffusion generation model is designed to fill in missing values in the time series data. The missing data generated by the model effectively improves radiologists’ judgment of pneumoconiosis progression. We trained our model using missing time series images to predict clinical outcomes. Experimental validation on two medical datasets shows that the AUC, sensitivity, specificity, and DSC achieved 90.33%, 87.89%, 85.01%, and 88.54%, respectively. These results highlight the competitive performance of our method across multiple evaluation metrics. Our model can capture the correlation between short-term/long-term/missing time series lesion features and time in pneumoconiosis images. This approach holds significant implications for predicting clinical outcomes and progression risk in pneumoconiosis, providing valuable guidance for the assessment and prognosis of pneumoconiosis.</p>

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Conditional Time Series Modeling for Pneumoconiosis Progression Risk Prediction with Missing Data

  • 期刊论文

  • 发表日期：

    2025年2月25日

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  • Xueting Ren,
  • Zijuan Zhao,
  • Liye Jia,
  • Juanjuan Zhao,
  • Baoping Jia,
  • Yan Qiang,
  • Huilan Zhao,
  • Huajie Yue

摘要

Pneumoconiosis is a serious occupational disease with high morbidity and disability rates. However, the evolution of pneumoconiosis is complex and changeable, and the course of most cases is incomplete, resulting in a lack of continuity in a large number of data samples, which makes it challenging for radiologists to accurately assess the development of the disease. We propose a conditional self-attention TimesNet as the backbone network for time series analysis tasks, aiming to improve prognosis prediction based on disease progression information in pneumoconiosis image data at different time periods. In our approach, we train the model using chest X-ray images of the same patient at different time points, incorporating a hierarchical attention structure and self-attention blocks to fully consider the contextual correlation information of consecutive time-point images. Additionally, diverse clinical features of patients are utilized as conditional inputs in disease progression prediction. The goal is to better learn the progression status of the disease and reflect the disease trajectory representation of missing time series data, enhancing the model’s predictive capabilities. Simultaneously, an adversarial diffusion generation model is designed to fill in missing values in the time series data. The missing data generated by the model effectively improves radiologists’ judgment of pneumoconiosis progression. We trained our model using missing time series images to predict clinical outcomes. Experimental validation on two medical datasets shows that the AUC, sensitivity, specificity, and DSC achieved 90.33%, 87.89%, 85.01%, and 88.54%, respectively. These results highlight the competitive performance of our method across multiple evaluation metrics. Our model can capture the correlation between short-term/long-term/missing time series lesion features and time in pneumoconiosis images. This approach holds significant implications for predicting clinical outcomes and progression risk in pneumoconiosis, providing valuable guidance for the assessment and prognosis of pneumoconiosis.