Abstract
Mining medical data has significantly gained interest in the recent years thanks to the advances in data mining and machine learning fields. In this work, we focus on a challenging issue in medical data mining: automatic diagnosis code assignment to discharge summaries, i.e., characterizing patient’s hospital stay (diseases, symptoms, treatments, etc.) with a set of codes usually derived from the International Classification of Diseases (ICD). We cast the problem as a machine learning task and we experiment some recent approaches based on the probabilistic topic models. We demonstrate the efficiency of these models in terms of high predictive scores and ease of result interpretation. As such, we show how topic models enable gaining insights into this field and provide new research opportunities for possible improvements.
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Notes
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Source codes from: http://www.cs.cmu.edu/~chongw/slda/ (sLDA) and https://github.com/myleott/JGibbLabeledLDA/ (labeledLDA).
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Dermouche, M., Velcin, J., Flicoteaux, R., Chevret, S., Taright, N. (2018). Supervised Topic Models for Diagnosis Code Assignment to Discharge Summaries. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_38
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