Masud Jakir Hossain Bhuiyan, Lin Ming-Chin
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Division of Neurosurgery, Department of Surgery, Shuang-Ho Hospital, Taipei, Taiwan.
Stud Health Technol Inform. 2020 Jun 16;270:1355-1356. doi: 10.3233/SHTI200439.
Automated coding and classification systems play a role in healthcare for quality of care. Our objective was to predict diagnosis code from medication list of electronic medical record (EMR) using convolutional neural network (CNN). We collected the clinical note from outpatient department (OPD) of Wanfang hospital, Taiwan of 2016 and used three physicians from three departments. The dataset was split into two parts, 90% for training and 10% for test cases. We used medication list as input and International Statistical Classification of Diseases 10 (ICD 10) code as output. After data preprocess, we used word2vector CNN to predict ICD 10 code. This study shows all the three physicians from three departments achieved better performance. The best performance of model was a physician from cardiology department achieved precision 69%, recall 89% and F measure 78%. We need to include more component such as text data, lab report for evaluation.
自动化编码和分类系统在医疗保健的护理质量方面发挥着作用。我们的目标是使用卷积神经网络（CNN）从电子病历（EMR）的用药清单中预测诊断代码。我们收集了台湾万方医院2016年门诊部（OPD）的临床记录，并使用了来自三个科室的三名医生。数据集被分成两部分，90%用于训练，10%用于测试用例。我们将用药清单作为输入，国际疾病分类第10版（ICD - 10）代码作为输出。经过数据预处理后，我们使用词向量CNN来预测ICD - 10代码。这项研究表明，来自三个科室的所有三名医生都取得了更好的表现。模型的最佳表现是一名心内科医生实现了精确率69%、召回率89%和F值78%。我们需要纳入更多组件，如文本数据、实验室报告进行评估。
