Predicting Complications in Critical Care Using Heterogeneous Clinical Data

Predicting Complications in Critical Care Using Heterogeneous Clinical Data

Patients in hospitals, particularly in critical care, are susceptible to many complications affecting morbidity and mortality. Digitized clinical data in electronic medical records can be effectively used to develop machine learning models to identify patients at risk of complications early and prov...

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Bibliographic Details
Main Authors: Vijay Huddar, Bapu Koundinya Desiraju, Vaibhav Rajan, Sakyajit Bhattacharya, Shourya Roy, Chandan K. Reddy
Format: Article
Language:English
Published: IEEE 2016-01-01
Series:IEEE Access
Subjects:
Clinical notes
topic models
heterogeneous data
multi–view learning
collective matrix factorization
postoperative respiratory failure
Online Access:https://ieeexplore.ieee.org/document/7593335/

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