A survey of machine learning applications in HIV clinical research and care.

A wealth of genetic, demographic, clinical and biomarker data is collected from routine clinical care of HIV patients and exists in the form of medical records available among the medical care and research communities. Machine learning (ML) methods have the ability to identify and discover patterns in complex datasets and predict future outcomes of HIV treatment. We survey published studies that make use of ML techniques in HIV clinical research and care. An advanced search relevant to the use of ML in HIV research was conducted in the PubMed biomedical database. The survey outcomes of interest include data sources, ML techniques, ML tasks and ML application paradigms. A growing trend in application of ML in HIV research was observed. The application paradigm has diversified to include practical clinical application, but statistical analysis remains the most dominant application. There is an increase in the use of genomic sources of data and high performance non-parametric ML methods with a focus on combating resistance to antiretroviral therapy (ART). There is need for improvement in collection of health records data and increased training in ML so as to translate ML research into clinical application in HIV management.