期刊名称：Interdisciplinary Description of Complex Systems - scientific journal
印刷版ISSN：1334-4676
出版年度：2017
卷号：15
期号：3
页码：199-208
出版社：Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu
摘要：We outline an automated computational and machine learning framework that predicts disease severity and stratifies patients. We apply our framework to available clinical data. Our algorithm automatically generates insights and predicts disease severity with minimal operator intervention. The computational framework presented here can be used to stratify patients, predict disease severity and propose novel biomarkers for disease. Insights from machine learning algorithms coupled with clinical data may help guide therapy, personalize treatment and help clinicians understand the change in disease over time. Computational techniques like these can be used in translational medicine in close collaboration with clinicians and healthcare providers. Our models are also interpretable, allowing clinicians with minimal machine learning experience to engage in model building. This work is a step towards automated machine learning in the clinic.
关键词：disease severity prediction; machine learning; computational technique; big data
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