Background

Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from other disorders. We developed a machine learning algorithm for the prediction of MDS one year prior to clinical diagnosis of the disease.

Methods

Retrospective analysis was performed on 790,470 patients over the age of 45 seen in the United States between 2007 and 2020. A gradient boosted decision tree model (XGB) was built to predict MDS diagnosis using vital signs, lab results, and demographics from the prior two years of patient data. The XGB model was compared to logistic regression (LR) and artificial neural network (ANN) models. The models did not use blast percentage and cytogenetics information as inputs. Predictions were made one year prior to MDS diagnosis as determined by International Classification of Diseases (ICD) codes, 9th and 10th revisions. Performance was assessed with regard to area under the receiver operating characteristic curve (AUROC).

Results

On a hold-out test set, the XGB model achieved an AUROC value of 0.87 for prediction of MDS one year prior to diagnosis, with a sensitivity of 0.79 and specificity of 0.80. The XGB model was compared against LR and ANN models, which achieved an AUROC of 0.838 and 0.832, respectively.

Conclusions

Machine learning may allow for early MDS diagnosis MDS and more appropriate treatment administration.

中文翻译：

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一种预测骨髓增生异常综合征风险的机器学习方法

背景

早期骨髓增生异常综合征 (MDS) 诊断可以让医生提供早期治疗，这可能会延迟 MDS 的进展并提高生活质量。然而，MDS 常常未被识别并且难以与其他疾病区分开来。我们开发了一种机器学习算法，用于在疾病临床诊断前一年预测 MDS。

方法

对 2007 年至 2020 年间在美国就诊的 790,470 名 45 岁以上患者进行了回顾性分析。构建了梯度提升决策树模型 (XGB) 以使用前两次的生命体征、实验室结果和人口统计学预测 MDS 诊断年的患者数据。XGB 模型与逻辑回归 (LR) 和人工神经网络 (ANN) 模型进行了比较。这些模型没有使用原始细胞百分比和细胞遗传学信息作为输入。根据国际疾病分类 (ICD) 代码第 9 版和第 10 版的确定，在 MDS 诊断前一年做出预测。根据接受者操作特征曲线 (AUROC) 下的面积评估性能。

结果

在保留测试集上，XGB 模型在诊断前一年预测 MDS 的 AUROC 值为 0.87，灵敏度为 0.79，特异性为 0.80。XGB 模型与 LR 和 ANN 模型进行了比较，分别达到了 0.838 和 0.832 的 AUROC。

结论

机器学习可能允许早期 MDS 诊断 MDS 和更合适的治疗管理。