AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI   Info

AG Audience

English

30 Minutes

Adverse drug reactions are a major public health concern, particularly when patients are prescribed multiple drugs at once. By leveraging datasets and machine learning models, we can predict potential drug-drug interactions more efficiently and at scale. However, many existing models face challenges such as data sparsity, poor feature extraction, and an inability to handle imbalanced datasets, which can lead to biased predictions. In my research, we developed ADEP to tackle these issues. ADEP combines the strengths of both adversarial training, through a discriminator, and auto-encoders to improve the accuracy and reliability of DDI predictions. In this talk, I will present this work's motivation, methodology, results, and broader implications.

Ina Geisler

+49 681 9325 1802

--email hidden

Zoom

passcode not visible

logged in users only