Diana Pholo

Data analytics for intelligent demand forecasting

Nowadays, we are all surrounded with data that is getting bigger in terms of volume, velocity and variety. The COVID-19 pandemic has also seen a growth in the use of dashboards and other data analysis tools. However, though many companies use data to get insights, they might not use those insights to drive their business strategy. So how can data and AI help you achieve your business goals? How can you transform your organisation so that business decisions are grounded in data? Is demand forecasting for you and your business? That is what this talk will attempt to answer.

The following topics will be covered: - The challenges of demand forecasting - The applications and benefits of demand forecasting using data and AI - Getting started with AI-augmented forecasting in your business - Creating a data culture within your organisation

Using machine learning and natural language processing to distinguish between lymphoma and COVID-19

Over 600,000 new lymphoma cases and around 280,000 lymphoma-related deaths were reported in 2020. The delayed diagnosis of lymphoma has long been a problem. However, the advent of the COVID-19 pandemic, which disrupted healthcare services worldwide, may have caused more significant delays in lymphoma diagnoses. Since lymphomas can sometimes present with symptoms like COVID-19 and can affect the lungs, there is also a risk of misdiagnosis. We collected 505 lymphoma and 180 COVID-19 case reports from ScienceDirect, curated them and applied boosting methods to classify each patient as having COVID-19 or lymphoma based on the patient’s age, gender and reported symptoms.

What will be covered in this talk: an overview of lymphoma and COVID-19, Python NLP tools, tree-based ensemble algorithms.