National Electronics and Computer Technology Center, Pathumthani, Thailand; Japan Advanced Institute of Science and Technology, Ishikawa, Japan; Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand.
National Electronics and Computer Technology Center, Pathumthani, Thailand.
Artif Intell Med. 2021 Mar;113:102033. doi: 10.1016/j.artmed.2021.102033. Epub 2021 Feb 12.
Sentiments associated with assessments and observations recorded in a clinical narrative can often indicate a patient's health status. To perform sentiment analysis on clinical narratives, domain-specific knowledge concerning meanings of medical terms is required. In this study, semantic types in the Unified Medical Language System (UMLS) are exploited to improve lexicon-based sentiment classification methods. For sentiment classification using SentiWordNet, the overall accuracy is improved from 0.582 to 0.710 by using logistic regression to determine appropriate polarity scores for UMLS 'Disorders' semantic types. For sentiment classification using a trained lexicon, when disorder terms in a training set are replaced with their semantic types, classification accuracies are improved on some data segments containing specific semantic types. To select an appropriate classification method for a given data segment, classifier combination is proposed. Using classifier combination, classification accuracies are improved on most data segments, with the overall accuracy of 0.882 being obtained.
与临床叙述中记录的评估和观察相关的情绪通常可以表明患者的健康状况。要对临床叙述进行情感分析，需要有关医学术语含义的特定于领域的知识。在这项研究中，利用统一医学语言系统 (UMLS) 中的语义类型来改进基于词汇的情感分类方法。对于使用 SentiWordNet 的情感分类，通过使用逻辑回归为 UMLS“疾病”语义类型确定适当的极性分数，整体准确性从 0.582 提高到 0.710。对于使用训练词汇的情感分类，当训练集中的疾病术语被替换为它们的语义类型时，某些包含特定语义类型的数据段的分类准确性得到提高。为了为给定的数据段选择适当的分类方法，提出了分类器组合。使用分类器组合，大多数数据段的分类准确性都得到了提高，总体准确性达到 0.882。
