E n S id N et: Enhanced Hybrid S iamese-Deep Network for grouping clinical trials into drug-development pathways (2021)

Abstract: Siamese Neural Networks have been widely used to perform similarity classification in multi-class settings. Their architecture can be used to group the clinical trials belonging to the same drug-development pathway along the several clinical trial phases. Here we present an approach for the unmet need of drug-development pathway reconstruction, based on an Enhanced hybrid Siamese-Deep Neural Network (EnSidNet). The proposed model demonstrates significant improvement above baselines in a 1-shot evaluation setting and in a classical similarity setting. EnSidNet can be an essential tool in a semi-supervised learning environment: by selecting clinical trials highly likely to belong to the same drug-development pathway it is possible to speed up the labelling process of human experts, allowing the check of a consistent volume of data, further used in the model’s training dataset.

## conclusion we present a new approach for the grouping of clinical trials into drug-development pathways. to meet this objective, we proposed 3 different neural network architectures. the best performing model was ensidnet, an enhanced hybrid siamese-deep neural network. ensidnet was used to develop a semisupervised learning pipeline using 1-shot evaluation and classification to group trials into existing or new pathways. human scoring would lead to the increase of the training size with adhoc positive and negative samples.