“Big Data in Public Health: Terminology, Machine Learning, and Privacy” explores the transformative potential of big data in public health, while addressing associated challenges. Big data, characterised by volume, variety, velocity, and veracity, can provide invaluable insights into health trends, disease outbreaks, and health determinants.
Machine learning, a subset of artificial intelligence, enables the analysis of large, complex datasets that traditional statistical methods struggle with. It’s particularly effective in detecting patterns and making predictions, hence its utility in public health.
Despite the potential, the use of big data in public health raises significant privacy concerns. The anonymisation of data is critical, but it’s increasingly difficult to guarantee due to the possibility of data linkage and re-identification. Additionally, the use of machine learning algorithms can unintentionally reinforce social biases present in the data, leading to unfair health outcomes.
To maximise the benefits and minimise the risks of big data in public health, there’s a need for a robust legal and ethical framework. This should ensure the responsible use of data, protect individual privacy, and promote equality in health outcomes. The document also highlights the importance of education and training in data science for public health professionals.
Go to source article: http://ftp.jrc.es/EURdoc/JRC85353.pdf