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Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science
Julio Montes-Torres1,2, José Luis Subirats1,3,2, Nuria Ribelles4,2
1Computer Science Department, Malaga University, Malaga, Spain.
Plos One
|August 18, 2016
Summary
This study introduces Online Survival Analysis (OSA), a user-friendly web tool for predictive survival modeling using machine learning. OSA empowers biomedical researchers with advanced Artificial Neural Network (ANN) techniques for clinical survival analysis.
Area of Science:
- Biomedical Informatics
- Machine Learning in Healthcare
- Clinical Data Science
Background:
- Predictive modeling is crucial in clinical survival analysis.
- Existing computational tools often lag behind machine learning advancements.
- Biomedical researchers need accessible tools for modern survival analysis.
Purpose of the Study:
- To bridge the gap between machine learning and clinical survival analysis.
- To introduce Online Survival Analysis (OSA), a web-based software.
- To provide an open-access, user-friendly platform for predictive survival modeling.
Main Methods:
- Development of OSA, a web-based software for survival analysis.
- Implementation of an Artificial Neural Network (ANN) for discrete time, predictive survival models.
- Integration of standard survival analysis features including curve generation and Cox regression.
Main Results:
- OSA offers an accessible platform for individual-level predictive survival models.
- The software generates customizable survival and hazard curves.
- Users can perform standard survival analyses and statistical tests via contingency tables.
Conclusions:
- OSA facilitates the application of advanced machine learning in clinical survival analysis.
- The tool enhances accessibility for biomedical researchers to cutting-edge predictive modeling.
- OSA provides a comprehensive solution for both standard and machine learning-based survival analysis.
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