A novel medical text classification model with Kalman filter for clinical decision making

Abstract

Medical text classification has long been a challenging issue in the construction of clinical decision support systems because medical texts contain medical terminologies and various medical abbreviations. This paper proposes an ALBERT-based fusion Kalman-filter model, named AFKF, to address word-level and sentence-level noises in electronic medical records. Specifically, a sliding window scheme is explored to deal with the coupling relationship among large sequences. Furthermore, we design a fusion block on the basis of Kalman filter to integrate representations of multiple segment sequences. Experiments indicate that our approach significantly outperforms the baseline methods with real-world medical texts. Our fusion strategy also improves the performance of other feature classification models in the medical text classification task.