Statement of problem

Tooth extraction therapy serves as a key initial step in many prosthodontic treatment plans. Dentists must make an appropriate decision on the tooth extraction therapy considering multiple determinants and whether a clinical decision support (CDS) model might help.

Purpose

The purpose of this retrospective records study was to construct a CDS model to predict tooth extraction therapy in clinical situations by using electronic dental records (EDRs).

Material and methods

The cohort involved 4135 deidentified EDRs of 3559 patients from the database of a prosthodontics department. Knowledge-based algorithms were first proposed to convert raw data from EDRs into structured data for feature extraction. Redundant features were filtered by a recursive feature-elimination method. The tooth extraction problem was then modeled alternatively as a binary or triple classification problem to be solved by 5 machine learning algorithms. Five machine learning algorithms within each model were compared, as well as the efficiency between 2 models. In addition, the proposed CDS was verified by 2 prosthodontists.

Results

The triple classification model outperformed the binary model with the F1 score of the Extreme Gradient Boost (XGBoost) algorithm as 0.856 and 0.847, respectively. The XGBoost outperformed the other 4 algorithms. The accuracy, precision, and recall of the XGBoost algorithm were 0.962, 0.865, and 0.830 in the binary classification and 0.924, 0.879, and 0.836 in the triple classification, respectively. The performance of the 2 prosthodontists was inferior to the models.

Conclusions

The CDS model for tooth extraction therapy achieved high performance in terms of decision-making derived from EDRs.

中文翻译：

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基于电子病历的拔牙治疗临床决策支持模型

问题陈述

拔牙治疗是许多修复治疗计划的关键初始步骤。牙医必须在考虑多个决定因素以及临床决策支持 (CDS) 模型是否有帮助的情况下，对拔牙治疗做出适当的决定。

目的

这项回顾性记录研究的目的是构建一个 CDS 模型，通过使用电子牙科记录 (EDR) 来预测临床情况下的拔牙治疗。

材料与方法

该队列涉及来自修复科数据库的 3559 名患者的 4135 个未识别 EDR。首先提出了基于知识的算法，将原始数据从 EDR 转换为结构化数据以进行特征提取。通过递归特征消除方法过滤冗余特征。然后将拔牙问题交替建模为二元或三元分类问题，由 5 种机器学习算法解决。比较了每个模型中的五种机器学习算法，以及两个模型之间的效率。此外，提议的 CDS 还得到了 2 名修复牙医的验证。

结果

三重分类模型优于二元模型，极限梯度提升（XGBoost）算法的 F1 分数分别为 0.856 和 0.847。XGBoost 优于其他 4 种算法。XGBoost 算法的准确率、准确率和召回率在二分类中分别为 0.962、0.865 和 0.830，在三分类中分别为 0.924、0.879 和 0.836。2 名修复牙医的表现不如模型。

结论

用于拔牙治疗的 CDS 模型在源自 EDR 的决策方面取得了高性能。