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

Duration: 0:45

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.


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