This seminar has now expired. Presentations are available to download at the appropriate links below.
Presentation 1: Changes in meat consumption patterns in the UK – investigation using loyalty card data
By: Patrycja Delong
Abstract: Vegetarian and vegan diets are increasing in popularity in the UK. Main motivations behind these dietary choices include health benefits along with concerns for animal welfare and the environment. Little is known however, on how people’s overall dietary patterns change when they reduce their meat consumption. Traditional food surveys can provide some information about global trends, but lack insight of changes in behaviour at an individual level. Moreover, they suffer from non-response bias and inaccuracy in self-reported food consumption. Using customer transaction data allows us to observe changes in purchasing behaviour of a cohort of households over time.
In this presentation, I am therefore going to be talking about how this project will apply machine learning techniques to identify households that reduce their meat consumption and examine the dietary patterns associated with that transition.
Download presentation: Patrycja Delong LIDA Seminar 30 Jan
Presentation 2: How can we detect Cancer Symptoms from Electronic Health Records?
By: Maab Ibrahim
Abstract: The vast growth in the availability of medical data and the rapid development of data analytics and AI tools and research in the healthcare domain gives us the opportunity, through this project, to facilitate the process of early diagnosis of cancer and cancer reoccurrence identification. We are hoping to do this by helping doctors improve medical decision making to achieve a better healthcare quality through the application of text analytics to Electronic Health Records (EHRs). These records include medical notes that doctors use to describe their patient’s information in unstructured English text. For cancer diagnosis, doctors use EHRs to record the signs and symptoms that are identified as critically related to the potential diagnosis of cancer.
I’ll be talking about how, in this project, we are applying text analytics to EHRs in order to construct a Natural Language Processing (NLP) model which will automatically extract and map NICE cancer symptoms from EHRs. This project is an augmentation of an ongoing project at PinPoint Data Science Ltd on Early Cancer Detection applied on NHS medical data. Not only does this work aim to make cancer diagnosis more efficient and accurate, but it could also impact on reducing costs and expenses in the diagnostic process.
Download presentation: Maab Ibrahim LIDA Seminar 30 Jan
Presentation 3:Assessing the presence of food deserts in the UK
By: Francisco Videira
Abstract: Food deserts are vulnerable areas, urban and rural, in which people experience physical and economic limitations in accessing healthy food. Food delivery systems began to appear in the ‘90s for people with reduced mobility who couldn’t go grocery shopping autonomously. In the following years, e-commerce began to unfold to target the wider public, due to the rising improvements in digital technologies. Paradoxically, there is some evidence that a new form of food desert might be emerging: the ‘e-food desert’. These are remote and rural catchments with poor access to physical stores and where food delivery systems are scarce or of poor quality.
By using web scraping techniques, socioeconomic data and spatial interaction modelling, this project aims to assess the presence of these areas and their characteristics, while also trying to reveal ‘hidden e-food deserts’ by simulating changes in availability and cost of delivery services.
Download presentation: Francisco Videira LIDA Seminar 30 Jan