用贝叶斯增强模型精准预测癌症诊疗需求趋势。
Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models
- 基于泊松-伽马共轭结构，引入残差提升的梯度增强机制。
- 在巴西卡里里数据上方向预测准确率比次优方法高38.25%。
- 适合医疗资源规划与长期趋势监测场景使用。
医疗时间序列的趋势预测对资源配置至关重要。本文提出一种贝叶斯框架，将每周门诊量建模为泊松过程，需求率设伽马先验。为增强适应性并捕捉持续趋势方向，引入基于伽马-对数正态共轭结构的残差型梯度提升机制。该方法可同时追踪短期与长期趋势变化，且保持共轭贝叶斯更新的解析可计算性。模型在巴西塞阿拉州卡里里真实癌症诊疗数据上评估，对比线性回归、ARIMA、朴素预测、LSTM神经网络和XGBoost等基线方法。结果表明，所提模型在趋势方向预测准确性上优于其他方法，部分情况下较次优方案提升达38.25%。
原文摘要 · Abstract (English)
Accurate trend forecasting in healthcare time series is essential for planning and resource allocation. This paper proposes a Bayesian framework for predicting oncology demand trends, modeling weekly appointments as a Poisson process with a Gamma prior to the demand rate. To enhance adaptability and capture persistent directional patterns, we incorporate a residual-based boosting mechanism grounded in a Gamma-Log-Normal conjugate structure. This boosting approach allows the model to track both short- and long-term trend shifts while maintaining the analytical tractability of conjugate Bayesian updating. The methodology was evaluated on real oncology service data from Cariri, Ceara, Brazil, and compared against established baselines, including linear regression, ARIMA, naive forecasting, LSTM neural networks, and XGBoost. Results showed that the proposed model outperforms competing methods in trend detection accuracy, with gains in terms of percentage of correct direction of 38.25% in relation to the second best approach in some cases.
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