 Automated interpretable computational bilogy in the clinic: a framework to predicst disease severity and stratify patients from clinical dataSoumya Banerjee ()
Interdisciplinary Description of Complex Systems - scientific journal, 2017, vol. 15, issue 3, 199-208 Abstract: 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. Keywords: disease severity prediction; machine learning; computational technique; big data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:zna:indecs:v:15:y:2017:i:3:p:199-208 Access Statistics for this article More articles in Interdisciplinary Description of Complex Systems - scientific journal from Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.