Strauss John, Peguero Arturo Martinez, Hirst Graeme
Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada.
Stud Health Technol Inform. 2013;192:1024.
In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.
在准备实施临床信息系统时，成瘾与心理健康中心（CAMH）的临床信息转型项目完成了多个准备步骤。需要一个自动化流程来辅助临床表格的繁重人工分析任务。我们针对一系列266份不同的临床表格使用了自然语言处理（NLP）和机器学习（ML）方法。在这项研究中，文档由特征向量表示。我们使用了四种机器学习算法来检查这些表格：聚类分析、k近邻（kNN）、决策树和支持向量机（SVM）。对每种算法的参数进行了优化。支持向量机表现最佳，精确率为64.6%。虽然我们没有找到任何一种方法在实际应用中足够准确，但据我们所知，这种处理表格的方法此前尚未在心理健康领域使用过。
