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A Model-Free Comorbidities-Based Events Prediction in ICU Unit

  • Tatiana Malygina
  • Ivan Drokin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 930)

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.

Keywords

Electronic health records Ontology-based regularization Neural networks Health care Learning (artificial intelligence) Neural nets Deep learning Deep neural network Electronic health record data Data models Diseases Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.The Laboratory of BioinformaticsITMO UniversitySt. PetersburgRussia
  2. 2.Intellogic Limited Liability Company (Intellogic LLC)MoscowRussia

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