Widespread adoption of precision medicine depends on an understanding of the implications of individual variations on drug type, dose, and response in various diseases, and access to high-quality patient data.
Herpes Simplex Virus (HSV) was used as an example of a disease that affects a huge population. Due to insufficient knowledge of the virus, there are still no vaccines for it despite large numbers of infections globally. Whilst there are many studies, researchers are unable to control the data provenance and quality. The World Health Organisation defined a better collection and management of epidemiologic data as the key first step towards an improved understanding of the virus and thus advancing research.
Data heterogeneity is a key problem in standardising epidemiologic data. Through developing a standardised patient data registry tools, we aimed to facilitate the collection of better quality data for a systematical study of HSV epidemiology. As a part of this project, we also developed an innovative machine learning algorithm for the identification of risk groups among people potentially infected with HSV.