Machine learning models to predict disease progression among veterans with hepatitis C virus.
<h4>Background</h4>Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models...
Main Authors: | Monica A Konerman, Lauren A Beste, Tony Van, Boang Liu, Xuefei Zhang, Ji Zhu, Sameer D Saini, Grace L Su, Brahmajee K Nallamothu, George N Ioannou, Akbar K Waljee |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2019-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0208141 |
Similar Items
-
Eliciting patient views on the allocation of limited healthcare resources: a deliberation on hepatitis C treatment in the Veterans Health Administration
by: Akbar K. Waljee, et al.
Published: (2020-05-01) -
Adapted time-varying covariates Cox model for predicting future cirrhosis development performs well in a large hepatitis C cohort
by: Lauren A. Beste, et al.
Published: (2021-12-01) -
Opioid and benzodiazepine prescription among patients with cirrhosis compared to other forms of chronic disease
by: Monica A Konerman, et al.
Published: (2019-06-01) -
Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.
by: Monica A Konerman, et al.
Published: (2017-01-01) -
Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding.
by: Wenshuo Liu, et al.
Published: (2020-01-01)