Background

The Japanese Orthopaedic Association National Registry (JOANR) was recently launched in Japan and is expected to improve the quality of medical care. However, surgeons must register ten detailed features for total hip arthroplasty, which is labor intensive. One possible solution is to use a system that automatically extracts information about the surgeries. Although it is not easy to extract features from an operative record consisting of free-text data, natural language processing has been used to extract features from operative records. This study aimed to evaluate the best natural language processing method for building a system that automatically detects some elements in the JOANR from the operative records of total hip arthroplasty.

Methods

We obtained operative records of total hip arthroplasty (n = 2574) in three hospitals and targeted two items: surgical approach and fixation technique. We compared the accuracy of three natural language processing methods: rule-based algorithms, machine learning, and bidirectional encoder representations from transformers (BERT).

Results

In the surgical approach task, the accuracy of BERT was superior to that of the rule-based algorithm (99.6% vs. 93.6%, p < 0.001), comparable to machine learning. In the fixation technique task, the accuracy of BERT was superior to the rule-based algorithm and machine learning (96% vs. 74%, p < 0.0001 and 94%, p = 0.0004).

Conclusions

BERT is the most appropriate method for building a system that automatically detects the surgical approach and fixation technique.

中文翻译：

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使用人工智能从手术记录中识别日本骨科协会国家登记处的数据元素

背景

日本骨科协会国家登记处 (JOANR) 最近在日本成立，有望提高医疗保健质量。然而，外科医生必须记录全髋关节置换术的十个详细特征，这是一项劳动密集型工作。一种可能的解决方案是使用自动提取有关手术信息的系统。尽管从由自由文本数据组成的手术记录中提取特征并不容易，但自然语言处理已被用于从手术记录中提取特征。本研究旨在评估构建一个系统的最佳自然语言处理方法，该系统可以从全髋关节置换术的手术记录中自动检测 JOANR 中的某些元素。

方法

我们获得了3家医院全髋关节置换术的手术记录（n = 2574），并针对两个项目：手术入路和固定技术。我们比较了三种自然语言处理方法的准确性：基于规则的算法、机器学习和来自 Transformer 的双向编码器表示 (BERT)。

结果

在手术入路任务中，BERT 的准确性优于基于规则的算法（99.6% vs. 93.6%，p < 0.001），与机器学习相当。在固定技术任务中，BERT 的准确性优于基于规则的算法和机器学习（96% vs. 74%，p < 0.0001 和 94%，p = 0.0004）。

结论

BERT 是构建自动检测手术入路和固定技术的系统的最合适方法。