Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models

TL;DR

This paper introduces a Bayesian boosting model for accurately forecasting oncology demand trends in healthcare, effectively capturing both short- and long-term shifts.

Contribution

It presents a novel boosting-based Bayesian conjugate framework that improves trend detection in healthcare time series data.

Findings

Model outperforms baseline methods in trend detection accuracy.

Achieves a 38.25% improvement in correct trend direction over second-best methods.

Demonstrates effectiveness on real oncology service data from Brazil.

Abstract

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.