ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients' disease history

Abstract

Given the rapid rate at which text data are being digitally gathered in the medical domain, there is growing need for automated tools that can analyze and classify clinical notes in electronic health records. One challenge in classifying clinical notes is the sparsity of co-occurrence patterns in short texts. Current approaches for short text classification are dependent on external information to improve classification performance. However, this is implausible owing to a lack of universal medical dictionaries. This study proposes the ETM (enrichment by topic modeling) algorithm, based on latent Dirichlet allocation, to smoothen the semantic representations of short texts. The ETM enriches text representation by incorporating probability distributions generated by an unsupervised algorithm into it. It considers the length of the original texts to enhance representation by using an internal knowledge acquisition procedure. When it comes to clinical predictive modeling, interpretability improves the acceptance of the model. Thus, for clinical short text classification, the ETM approach employs an initial TFiDF (term frequency inverse document frequency) representation, where we use the support vector machine and neural network algorithms for the classification task. We conducted three sets of experiments on a dataset consisting of clinical cardiovascular notes from the Netherlands to test the classification performance of the proposed method in comparison with prevalent approaches. The results show that the proposed ETM approach outperformed state-of-the-art baselines.

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ORCID for Peter Van Der Heijden: orcid.org/0000-0002-3345-096X

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