Title: The Usage and Improvement of Neurosymbolic AI for Biomedical Applications
Speaker: Lauren DeLong
Neurosymbolic Artificial Intelligence (AI) describes the combination of logic and rule-based approaches with deep learning. Often, the goal of neurosymbolic AI is to achieve comparable performance to current deep learning methods while simultaneously maintaining interpretability; this makes neurosymbolic AI both practically and ethically suitable for biomedical applications. However, few previous studies have attempted to apply neurosymbolic AI within the biomedical domain. Furthermore, many neurosymbolic approaches have unique abilities that neither symbolic nor neural approaches had alone. Many of these characteristics fit the unique challenges imposed by biomedical data. For example, some neurosymbolic approaches have the ability to represent meaningful relationships between data types, which could be used as a novel way to handle multimodal biomedical data fusion. Additionally, other neurosymbolic methods use a neural network to learn domain-specific rules; this opens the possibility to mine patterns from multi-relational biomedical data. The aims of this thesis, therefore, are to produce some of the first studies using neurosymbolic AI for biomedical applications as well as demonstrate ways to utilize the unique abilities of neurosymbolic methods for common challenges surrounding biomedical data.