Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, USA.
Department of Computer Science, Wayne State University, Detroit, MI, USA.
BMC Med Inform Decis Mak. 2021 Nov 23;21(1):328. doi: 10.1186/s12911-021-01665-w.
In surgical department, CPT code assignment has been a complicated manual human effort, that entails significant related knowledge and experience. While there are several studies using CPTs to make predictions in surgical services, literature on predicting CPTs in surgical and other services using text features is very sparse. This study improves the prediction of CPTs by the means of informative features and a novel re-prioritization algorithm.
The input data used in this study is composed of both structured and unstructured data. The ground truth labels (CPTs) are obtained from medical coding databases using relative value units which indicates the major operational procedures in each surgery case. In the modeling process, we first utilize Random Forest multi-class classification model to predict the CPT codes. Second, we extract the key information such as label probabilities, feature importance measures, and medical term frequency. Then, the indicated factors are used in a novel algorithm to rearrange the alternative CPT codes in the list of potential candidates based on the calculated weights.
To evaluate the performance of both phases, prediction and complementary improvement, we report the accuracy scores of multi-class CPT prediction tasks for datasets of 5 key surgery case specialities. The Random Forest model performs the classification task with 74-76% when predicting the primary CPT (accuracy@1) versus the CPT set (accuracy@2) with respect to two filtering conditions on CPT codes. The complementary algorithm improves the results from initial step by 8% on average. Furthermore, the incorporated text features enhanced the quality of the output by 20-35%. The model outperforms the state-of-the-art neural network model with respect to accuracy, precision and recall.
We have established a robust framework based on a decision tree predictive model. We predict the surgical codes more accurately and robust compared to the state-of-the-art deep neural structures which can help immensely in both surgery billing and scheduling purposes in such units.
在外科部门，CPT 代码分配是一项复杂的手动工作，需要具备相关的知识和经验。虽然有几项研究使用 CPT 来进行手术服务的预测，但关于使用文本特征预测手术和其他服务中的 CPT 的文献却很少。本研究通过提供信息特征和一种新颖的重新排序算法来改进 CPT 的预测。
本研究使用的输入数据包括结构化和非结构化数据。地面真实标签（CPT）是通过使用相对价值单位从医疗编码数据库中获得的，相对价值单位表示每个手术病例中的主要手术程序。在建模过程中，我们首先利用随机森林多类分类模型来预测 CPT 代码。其次，我们提取关键信息，如标签概率、特征重要性度量和医疗术语频率。然后，在所指示的因素的基础上，使用一种新颖的算法根据计算出的权重重新排列潜在候选者列表中的替代 CPT 代码。
为了评估预测和补充改进两个阶段的性能，我们报告了 5 种主要手术病例专科数据集的多类 CPT 预测任务的准确率分数。随机森林模型在预测主要 CPT（准确率@1）相对于 CPT 集（准确率@2）时执行分类任务，准确率为 74-76%，同时对 CPT 代码进行了两种过滤条件。互补算法平均提高了初始步骤的结果 8%。此外，纳入的文本特征提高了输出质量 20-35%。该模型在准确性、精度和召回率方面优于最先进的神经网络模型。
我们已经建立了一个基于决策树预测模型的强大框架。与最先进的深度神经网络结构相比，我们更准确和稳健地预测了手术代码，这对这些单位的手术计费和调度目的非常有帮助。
