Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models
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...
| Authors | Ademir Batista dos Santos Neto, Tiago Alessandro Espinola Ferreira, Paulo Renato Alves Firmino |
|---|---|
| Primary category | stat.ML |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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