We presented an artificial intelligence-based model to predict annual effective dose (AED) value of health workers. Potential factors affecting AED and the results of annual blood tests were collected from 91 radiation workers. Filter-based feature selection strategy revealed that the eight factors plate, red cell distribution width (RDW), educational degree, nonacademic course in radiation protection (hour), working hours per month, department and the number of procedures done per year and work in radiology department or not (0,1) were the most important predictors for AED. The prediction model was developed using a multilayer perceptron neural network and these prediction parameters as inputs. The model provided favorable accuracy in predicting AED value while a regression model did not. There was a strong linear relationship between the predicted AED values and the measured doses (R-value =0.89 for training samples and 0.86 for testing samples). These results are promising and show that artificial neural networks can be used to improve/facilitate dose estimation process.

中文翻译：

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一种基于人工神经网络的模型，用于预测职业暴露于不同电离辐射水平的医护人员的年剂量。

我们提出了一种基于人工智能的模型来预测卫生工作者的年有效剂量 (AED) 值。收集了 91 名放射工作人员的影响 AED 的潜在因素和年度血液检查结果。基于过滤器的特征选择策略揭示了八个因素板块、红细胞分布宽度（RDW）、教育程度、辐射防护非学术课程（小时）、每月工作时间、部门和每年完成的程序数量和工作放射科与否 (0,1) 是 AED 最重要的预测因素。预测模型是使用多层感知器神经网络和这些预测参数作为输入开发的。该模型在预测 AED 值方面提供了良好的准确性，而回归模型则没有。预测的 AED 值与测量的剂量之间存在很强的线性关系（训练样本的 R 值 = 0.89，测试样本的 R 值 = 0.86）。这些结果很有希望，并表明人工神经网络可用于改进/促进剂量估计过程。