Clinical trials are pivotal yet costly processes, often spanning multiple years and requiring substantial expenses, motivating predictive models to identify likely-to-fail drugs early and save resources. Recent approaches leverage deep learning to integrate multimodal data for clinical outcome prediction; however, they rely heavily on manually designed modality-specific encoders, limiting their adaptability to new modalities and ability to effectively share information across modalities. To address these challenges, we propose a multimodal mixture-of-experts (LIFTED) framework. Specifically, LIFTED transforms modality-specific data into natural language descriptions, encoded via unified, noise-resilient encoders. A sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities, extracting consistent representations. Finally, another mixture-of-experts module dynamically integrates these modality representations, emphasizing critical information. Experiments show that LIFTED significantly outperforms baseline methods in predicting clinical trial outcomes across all phases, highlighting the effectiveness of our proposed approach.

Word embeddings have become a mainstream tool in statistical natural language processing. Practitioners often use pre-trained word vectors, which were trained on large generic text corpora, and which are readily available on the web. However, pre-trained word vectors oftentimes lack important words from specific domains. It is therefore often desirable to extend the vocabulary and embed new words into a set of pre-trained word vectors. In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings. We build on the established view of word embeddings as matrix factorizations to present a spectral algorithm for this task. Experiments on several domain-specific corpora with specialized vocabularies demonstrate that our method is able to embed the new words efficiently into the original embedding space. Compared to competing methods, our method is faster, parameter-free, and deterministic.