Comparative Analysis of Text Classification Approaches in Electronic Health Records
This work addresses the challenge of text classification in electronic health records for clinical research, but it is incremental as it focuses on comparative analysis without introducing new methods.
The study compared traditional and modern text classification methods on electronic health records, finding that tailored traditional approaches can match or outperform newer contextual embeddings like BERT on four tasks.
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.