Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance.
10.4258/hir.2017.23.4.271
- Author:
Yashik SINGH
1
Author Information
1. Department of Telehealth, Nelson R Mandela School of Medicine, University of KwaZulu Natal, South Africa. singhyashik@gmail.com
- Publication Type:Original Article
- Keywords:
Medical Informatics;
Health Informatics;
Computational Biology;
Artificial Intelligence;
Clinical Informatics;
Machine Learning
- MeSH:
Acquired Immunodeficiency Syndrome;
Artificial Intelligence;
Computational Biology;
Developing Countries;
Drug Resistance*;
Genome;
HIV*;
Machine Learning*;
Medical Informatics;
Mutation Rate
- From:Healthcare Informatics Research
2017;23(4):271-276
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance. METHODS: A genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured. RESULTS: This study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%. CONCLUSIONS: The study shows that there is a significant improvement in the prediction ability of ANRS gold standard.
