All Outputs (5)

Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake (2023)
Conference Proceeding
Harvey, J., Long, G., Welham, S., Mansilla, R., Rose, P., Thomas, M., …Goulding, J. (2023). Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake. In Proceedings: 2023 IEEE International Conference on Big Data (BigData) (4530-4538). https://doi.org/10.1109/BigData59044.2023.10386220

Anthocyanins are a class of polyphenols that have received widespread recent attention due to their potential health benefits. However, estimating the dietary intake of anthocyanins at a population level is a challenging task, due to the difficulty o... Read More about Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake.

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models (2023)
Journal Article
Dolan, E., Goulding, J., Marshall, H., Smith, G., Long, G., & Tata, L. J. (2023). Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models. Nature Communications, 14, Article 7258. https://doi.org/10.1038/s41467-023-42776-4

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of... Read More about Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models.

Qualitative Investigation of the Novel Use of Shopping Loyalty Card Data in Medical Decision Making (2023)
Book Chapter
Lang, A., Dolan, E., Tata, L., & Goulding, J. (2023). Qualitative Investigation of the Novel Use of Shopping Loyalty Card Data in Medical Decision Making. In M. Melles, A. Albayrak, & R. H. Goossens (Eds.), Convergence: Breaking Down Barriers Between Disciplines: Proceedings of the International Conference on Healthcare Systems Ergonomics and Patient Safety, HEPS2022. Springer. https://doi.org/10.1007/978-3-031-32198-6_11

This paper describes early results of a small qualitative study investigating the potential impact of shopping loyalty card data (SLCD) in the diagnostic pathway for ovarian cancer. There is early evidence that pharmaceutical products such as pain re... Read More about Qualitative Investigation of the Novel Use of Shopping Loyalty Card Data in Medical Decision Making.

Data donation of individual shopping data to help predict the occurrence of disease: A pilot study linking individual loyalty card and health survey data to investigate COVID-19 (2023)
Journal Article
Dolan, E., Goulding, J., & Skatova, A. (2023). Data donation of individual shopping data to help predict the occurrence of disease: A pilot study linking individual loyalty card and health survey data to investigate COVID-19. International Journal of Population Data Science, 8(3), https://doi.org/10.23889/ijpds.v8i3.2273

Introduction & Background Previous studies have found shopping data could increase the predictive accuracy of disease surveillance systems and illuminate behavioural responses in the self-management of symptoms of disease. Yet, accessing individual... Read More about Data donation of individual shopping data to help predict the occurrence of disease: A pilot study linking individual loyalty card and health survey data to investigate COVID-19.

Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey (2023)
Journal Article
Dolan, E., Goulding, J., Tata, L., & Lang, A. (2023). Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey. JMIR Cancer, 9, Article e37141. https://doi.org/10.2196/37141

Background Shopping data can be analysed using machine learning techniques to study population health. It is unknown if use of such methods can successfully investigate pre-diagnosis purchases linked to self-medication of symptoms of ovarian cance... Read More about Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey.