Kaur Rajvir, Ginige Jeewani Anupama
School of Computing, Engineering & Mathematics, Western Sydney University, Australia.
Stud Health Technol Inform. 2018;252:73-79.
Clinical coding is done using ICD-10-AM (International Classification of Diseases, version 10, Australian Modification) and ACHI (Australian Classification of Health Interventions) in acute and sub-acute hospitals in Australia for funding, insurance claims processing and research. The task of assigning a code to an episode of care is a manual process. This has posed challenges due to increase set of codes, the complexity of care episodes, and large training and recruitment costs of clinical coders. Use of Natural Language Processing (NLP) and Machine Learning (ML) techniques is considered as a solution to this problem. This paper carries out a comparative analysis on a selected set of NLP and ML techniques to identify the most efficient algorithm for clinical coding based on a set of standard metrics: precision, recall, F-score, accuracy, Hamming loss and Jaccard similarity.
在澳大利亚的急症和亚急症医院中，临床编码使用ICD - 10 - AM（国际疾病分类第10版，澳大利亚修订版）和ACHI（澳大利亚健康干预分类）来进行资金筹集、保险理赔处理及研究。为一次护理事件分配代码的任务是一个手动过程。由于代码集的增加、护理事件的复杂性以及临床编码员高昂的培训和招聘成本，这带来了挑战。自然语言处理（NLP）和机器学习（ML）技术的应用被视为解决这一问题的方案。本文基于一组标准指标：精确率、召回率、F值、准确率、汉明损失和杰卡德相似度，对一组选定的NLP和ML技术进行了比较分析，以确定用于临床编码的最有效算法。
