Abstract
Is it true that patients with similar conditions get similar diagnoses? In this paper we present a natural language processing (NLP) method that can be used to validate this claim. We (1) introduce a method for representation of medical visits based on free-text descriptions recorded by doctors, (2) introduce a new method for segmentation of patients’ visits, (3) present an application of the proposed method on a corpus of 100,000 medical visits and (4) show tools for interpretation and exploration of derived knowledge representation. With the proposed method we obtained stable and separated segments of visits which were positively validated against medical diagnoses. We show how the presented algorithm may be used to aid doctors in their practice.
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CITATION STYLE
Dobrakowski, A. G., Mykowiecka, A., Marciniak, M., Jaworski, W., & Biecek, P. (2020). Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12117 LNAI, pp. 45–55). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59491-6_5
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