医疗信函自动编码与可解释性的比较研究
A Comparative Study on Automatic Coding of Medical Letters with Explainability
AI总结：
本研究比较了在本地计算机上使用NLP和ML模型自动编码医疗信函的方法，结合可解释性，利用MIMIC-III和HAN/HLAN模型，为97.98%的代码提供了有用信息，推动了临床编码的自动化实践。
摘要：
本研究旨在探索利用自然语言处理（NLP）和机器学习（ML）技术，在可视化可解释性和轻量级本地计算机设置下，实现医疗信函的自动编码。目前，在临床环境中，编码是一个手动过程，涉及为患者文件中的每种状况、程序和药物分配代码（例如，使用SNOMED CT代码56265001表示心脏病）。该领域已有使用最先进ML模型进行自动编码的初步研究；然而，由于模型的复杂性和规模，实际部署尚未实现。为了进一步促进自动编码实践的可能性，我们在本地计算机设置中探索了一些解决方案；此外，我们探索了可解释性功能以实现AI模型的透明度。我们使用了公开可用的MIMIC-III数据库和HAN/HLAN网络模型进行ICD代码预测。我们还试验了ICD与SNOMED CT知识库之间的映射。在我们的实验中，模型为97.98%的代码提供了有用信息。这项研究的结果可以为在实际中实施自动临床编码（例如在医院环境中，在临床医生使用的本地计算机上）提供一些启示，项目页面：https://github.com/Glenj01/Medical-Coding。
英文摘要：
This study aims to explore the implementation of Natural Language Processing (NLP) and machine learning (ML) techniques to automate the coding of medical letters with visualised explainability and light-weighted local computer settings. Currently in clinical settings, coding is a manual process that involves assigning codes to each condition, procedure, and medication in a patient's paperwork (e.g., 56265001 heart disease using SNOMED CT code). There are preliminary research on automatic coding in this field using state-of-the-art ML models; however, due to the complexity and size of the models, the real-world deployment is not achieved. To further facilitate the possibility of automatic coding practice, we explore some solutions in a local computer setting; in addition, we explore the function of explainability for transparency of AI models. We used the publicly available MIMIC-III database and the HAN/HLAN network models for ICD code prediction purposes. We also experimented with the mapping between ICD and SNOMED CT knowledge bases. In our experiments, the models provided useful information for 97.98\% of codes. The result of this investigation can shed some light on implementing automatic clinical coding in practice, such as in hospital settings, on the local computers used by clinicians , project page \url{https://github.com/Glenj01/Medical-Coding}.
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