Comparison of Different Feature Engineering Methods for Automated ICD Coding

ICD coding is quite important as it serves as basis for a wide range of economic and academic applications. Currently, manual coding is mainly adopted, which faces several limits like being time-consuming and prone to error, and this makes automated ICD coding via machine learning a hot research topic. As an inevitable phase during machine learning, feature engineering plays a crucially important role in leading to promising coding performance. Although have reached enlightening conclusions, existing studies lacked comparison of different feature engineering methods. Finding out what methods under what circumstances perform better can be quite helpful in promoting practical applications of automated coding. The investigators will implement this study based on inpatient' data collected from electronic medical records from Fuwai Hospital, the world's largest medical center for cardiovascular disease. Bag-of-words, word2cec and roberta will be respectively used to extracted features from training data. Then code-wise logistic regression classifiers and support vector machine classifiers will be trained to auto-assign codes. Afterwards, performances of the models on test data will be evaluated.