Using machine learning and natural language processing to distinguish between lymphoma and COVID-19
Speaker: Diana Pholo
Track: Data Science
Type: Remote Talk
Room: Talk Room 3 (Remote Talks)
Time: Oct 14 (Fri): 12:15
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.