Abstract
In this work we focus on recently introduced “medical concept vectors” (MCV) extracted from electronic health records (EHR), explore in similar manner several methods useful for patient’s medical history events prediction and provide our own novel state-of-the-art method to solve this problem. We use MCVs to analyze publicly-available EHR de-identified data, with strong focus on fair comparison of several different models applied to patient’s death, heart failure and chronic liver diseases (cirrhosis and fibrosis) prediction tasks. We propose ontology-based regularization method that can be used to pre-train MCV embeddings. The approach we use to predict these diseases and conditions can be applied to solve other prediction tasks.
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Notes
- 1.
Corresponding ICD-9 codes are: 398.91, 402.01–402.91, 404.01–404.93, 428.0–428.43.
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Malygina, T., Drokin, I. (2018). A Model-Free Comorbidities-Based Events Prediction in ICU Unit. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_10
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