AUTOMATED INTERPRETABLE COMPUTATIONAL
PATIENTS FROM CLINICAL DATA
Soumya BanerjeeUniversity of Oxford
Oxford, United Kingdom
Montclair, United States of America
|INDECS 15(3), 199-208, 2017
Full text available here.
Received: 7th September 2017.
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.
JEL: I19, C63