Comparative Analysis of Text Classification Approaches in Electronic Health Records (2020)

Abstract: Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification tasks, notably due to the particular nature of the medical lexicon and language used in clinical records. Recent advances in embedding methods have shown promising results for several clinical tasks, yet there is no exhaustive comparison of such approaches with other commonly used word representations and classification models. In this work, we analyse the impact of various word representations, text pre-processing and classification algorithms on the performance of four different text classification tasks. The results show that traditional approaches, when tailored to the specific language and structure of the text inherent to the classification task, can achieve or exceed the performance of more recent ones based on contextual embeddings such as BERT.

## introduction clinical text classification is an important task in natural language processing (nlp) @xcite , where it is critical to harvest data from electronic health records (ehrs) and facilitate its use for decision support and translational research. thus, it is increasingly used to retrieve and organize information from the unstructured portions of ehrs @xcite . examples include tasks such as: (1) detection of smoking status @xcite ; (2) classification of medical concept mentions into family * these two authors contributed equally. text classification performed on medical records presents specific challenges compared to the general domain (such as newspaper texts), including dataset imbalance, misspellings, abbreviations or semantic ambiguity @xcite . despite recent advances in nlp, including neuralnetwork based word representations such as bert @xcite , few approaches have been extensively tested in the medical domain and rulebased algorithms remain prevalent @xcite . furthermore, there is no consensus on which word representation is best suited to specific downstream classification tasks @xcite . the purpose of this study is to analyse the impact of numerous word representation methods (bagof-word versus traditional and contextual word embeddings) as well as classification approaches (deep learning versus traditional machine learning methods) on the performance of four different text classification tasks. to our knowledge this is the first paper to test a comprehensive range of word representation, text pre-processing and classification methods combinations on several medical text tasks. ## conclusion in this article we have explored the performance of various word representation approaches (comparing bag-of-words to traditional and contextual embeddings trained on both specific and general corpora, combined with various text pre-processing and tokenizing methods) as well as classification algorithms on four different text classification tasks, all based on publicly available datasets. a detailed performance comparison on these four tasks highlighted the efficacy of contextual embeddings when compared to traditional methods when no customization is possible, whether these embeddings are trained on specific or general corpora. when combined with appropriate entity extraction tasks and specific domain embeddings, bi-lstm outperforms contextual embeddings. across all classification algorithms, text pre-processing and tokenization approaches showed limited impact for the task and embedding tested, suggesting a rule of thumb to opt for the least time and resource intensive method.