Predicting therapeutic outcomes in Rheumatoid Arthritis using real-world pharmacogenetic and clinical data
- Resource Type
- Conference
- Authors
- Hernandez, Fabian; Nino, Luis Fernando; Aristizabal, Fabio
- Source
- 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2020 IEEE International Conference on. :426-431 Dec, 2020
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Support vector machines
Sociology
Partitioning algorithms
Logistics
Vegetation
Predictive models
Machine learning algorithms
Rheumatoid Arthritis
Pharmacogenetics
Machine Learning
Data Mining
Real-World Data
- Language
OBJECTIVE: To develop a pharmacogenetic and clinical model to predict effectiveness outcomes in a cohort of patients diagnosed with rheumatoid arthritis (RA) treated with methotrexate. METHODS: Five machine learning methods were tested on the patient data set with previous preprocessing for dataset cleaning and feature selection: Logistic regression, decision trees, random forests, Support Vector Machines (SVM) and Artificial Neural Networks (ANN). The models were applied in a cohort of 155 patients treated with MTX which was derived in a training (124 patients) and a test cohort (31 patients). Both clinical variables and genetic variations were included. The chosen outcome was the therapy response measured as a DAS 28