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

被引：0

作者：

Malygina, Tatiana ^{[1
,2
]}

Drokin, Ivan ^{[2
]}

机构：

[1] ITMO Univ, Lab Bioinformat, Kronverkskiy Pr 49, St Petersburg 197101, Russia

[2] Intell Ltd Liabil Co Intell LLC, Terr Skolkovo Innovat Ctr, Off 1-334-63,Bldg 1,42 Bolshoi Blvd, Moscow 121205, Russia

来源：

ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE (AINL 2018) | 2018年 / 930卷

关键词：

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;

D O I：

10.1007/978-3-030-01204-5_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：98 / 109

页数：12

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