We have investigated methodologies for predicting radiologists' workload in a short time interval by adopting a machine learning technique. Predicting for shorter intervals requires lower execution time combined with higher accuracy. To deal with this issue, an ensemble model is proposed with the fixed‐batch‐training method. To excel in the execution time, a fixed‐batch‐training method is used. On the other hand, the ensemble of multiple machine learning algorithms provides higher accuracy. The experimental result shows that this predictive model can produce at least 10% higher accuracy in comparison with the other available widely used short‐term time series forecasting models. In the studied medical system, this gain in accuracy for the earlier prediction of workload can reduce the Medicare relative value unit cost by $1.1 million annually, which we have formulated and shown in this paper. The proposed batch‐trained ensemble of experts model has also provided at least a 6% improvement in execution time compared with the other studied models.

中文翻译：

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短期时间序列分析的因果模型，以预测传入的Medicare工作量

我们已经研究了通过采用机器学习技术在短时间内预测放射科医生工作量的方法。预测更短的间隔需要更短的执行时间以及更高的精度。为了解决这个问题，提出了一种采用固定分批训练的集成模型。为了更好地执行时间，使用了固定分批训练方法。另一方面，多种机器学习算法的结合提供了更高的准确性。实验结果表明，与其他广泛使用的短期时间序列预测模型相比，该预测模型可产生至少10％的精度。在所研究的医疗系统中，对工作量的较早预测所获得的准确性提高，可使Medicare相对价值单位成本每年减少110万美元，我们已经在本文中阐述并展示了这一点。与其他研究模型相比，该专家模型建议的分批训练的集成还至少使执行时间缩短了6％。