ABSTRACT. Empirical evidence on big health care data in clinical natural language processing has been scarcely documented in the literature. Using and replicating data from 3M Health Information Systems, Amazon, GE Healthcare, Maruti Techlabs, McKinsey, Mindfields, MIT Technology Review Insights, and TDWI, I performed analyses and made estimates regarding high-potential natural language processing (NLP) use cases in healthcare (%, relevance). Data were analyzed using structural equation modeling.

Keywords: deep learning algorithm; big data; healthcare; clinical natural language processing

How to cite: Ionescu, Daniela (2020). “Deep Learning Algorithms and Big Health Care Data in Clinical Natural Language Processing,” Linguistic and Philosophical Investigations 19: 86–92. doi: 10.22381/LPI1920204

Received 15 December 2019 • Received in revised form 2 March 2020
Accepted 6 March 2020 • Available online 16 March 2020

Daniela Ionescu
This email address is being protected from spambots. You need JavaScript enabled to view it.
The University of Bucharest, Romania

Home | About Us | Events | Our Team | Contributors | Peer Reviewers | Editing Services | Books | Contact | Online Access

© 2009 Addleton Academic Publishers. All Rights Reserved.

Joomla templates by Joomlashine