一項關於醫學信件自動編碼與可解釋性的比較研究

A Comparative Study on Automatic Coding of Medical Letters with Explainability

July 18, 2024

作者: Jamie Glen, Lifeng Han, Paul Rayson, Goran Nenadic

cs.AI

摘要

本研究旨在探索利用自然語言處理（NLP）和機器學習（ML）技術來自動編碼醫療信件，並實現可視化的可解釋性和輕量級的本地計算機設置。目前在臨床環境中，編碼是一個手動過程，涉及為患者的文件（例如，使用 SNOMED CT 代碼的 56265001 心臟病）中的每個疾病、程序和藥物分配代碼。在這一領域已有關於使用最先進的 ML 模型進行自動編碼的初步研究；然而，由於模型的複雜性和大小，尚未實現實際部署。為了進一步促進自動編碼實踐的可能性，我們在本地計算機環境中探索了一些解決方案；此外，我們探討了可解釋性功能以提高 AI 模型的透明度。我們使用了公開可用的 MIMIC-III 數據庫和 HAN/HLAN 網絡模型進行 ICD 代碼預測。我們還對 ICD 和 SNOMED CT 知識庫之間的映射進行了實驗。在我們的實驗中，模型為 97.98% 的代碼提供了有用信息。這一研究結果可以為實踐中實現自動臨床編碼提供一些啟示，例如在醫院環境中，醫生使用的本地計算機上，項目頁面 https://github.com/Glenj01/Medical-Coding。

English

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 https://github.com/Glenj01/Medical-Coding.