- 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 ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 645.2kb)
-
- Publisher copy:
- 10.7906/indecs.15.3.4
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- Publisher:
- Croatian Interdisciplinary Society Publisher's website
- Journal:
- Interdisciplinary Description of Complex Systems Journal website
- Volume:
- 15
- Issue:
- 3
- Pages:
- 199-208
- Publication date:
- 2017-11-01
- Acceptance date:
- 2017-10-06
- DOI:
- ISSN:
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1334-4676
- Pubs id:
-
pubs:742131
- URN:
-
uri:35270a40-673d-45c5-81f0-7d1994b7dd45
- UUID:
-
uuid:35270a40-673d-45c5-81f0-7d1994b7dd45
- Local pid:
- pubs:742131
- Copyright holder:
- Soumya Banerjee
- Copyright date:
- 2017
- Notes:
- This article is licensed under a Creative Commons Attribution 4.0 International License.
Journal article
Automated interpretable computational biology in the clinic: a framework to predict disease severity and stratify patients from clinical data
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