Automated International Classification of Diseases (ICD) coding tasks are designed to assign diagnosis and procedure codes to patients’ electronic medical records (EMRs). Recent works have applied deep neural network models and related techniques for code assignment to clinical notes. However, most existing methods have overlooked the advantageous complementary information presented in the tabular data from EMRs and the Wikipedia knowledge. Therefore, we propose a Multimodal Knowledge Fusion Network (MKFN) to effectively integrate clinical notes, tabular data, and Wikipedia knowledge, enhancing the model’s predictive capabilities. We incorporate structured tabular data and clinical notes into an initial multimodal representation using label attention and self-attention mechanisms. We propose a knowledge fusion network to leverage tabular data and Wikipedia knowledge for accurate predictions when code descriptions are absent in clinical notes. Experiments on the MIMIC dataset show that our proposed model achieves competitive results among existing ICD coding methods.

The identification of protein complexes can help understand cellular organization principles and the mechanism of biological evolution. In recent years, researchers have proposed numerous computational methods to identify protein complexes through their interaction networks. Most of these methods identify protein complexes based on the topological structure of the PPI network. However, the topological structure contained in the PPI network is very complicated, and the applicability of advanced representation learning methods has not been researched in depth. This paper proposes a contrastive self-supervised representation learning method to identify protein complexes. Our method uses a mix-hop aggregator based on graph neural network (GNN) to capture high-order interaction in the PPI network and leverage a contrastive self-supervised method to train our model without introducing protein labels. Then, we get the vector representation for each protein and construct a weighted PPI network based on the vector representation similarity. Finally, we apply clustering aggregation to identify protein complexes based on a weighted PPI network. In order to access our method, different PPI networks, DIP, Kroganl4k and Biogrid, are used as datasets. By comparing the competing methods including COACH, CMC, MCODE, ClusterONE, GANE and COAN, experimental results show that our method outperforms classic and state-of-the-art methods.

In this paper, we focus on the Protein-Protein Interaction Article Classification (PPIAC) problem. In order to make better use of domain knowledge, we propose a Hierarchical Knowledge-aware Hybrid Neural Network (HKaHNN) model to classify PPI articles. Inspired by existing work, we introduce two kinds of knowledge embeddings and design a Hierarchical Knowledge-aware Attention (HKaATT) component which implements the interaction between the original token representations and external knowledge from the token-level and sentence-level respectively. In addition, in order to improve the anti-interference ability of the model, we adopt the adversarial training strategy. Our model achieves competitive performance on BioCreative II and BioCreative III corpora, with Fl-scores of 83.67% and 68.86%, respectively.

As the biomedical literature increases exponentially, biomedical named entity recognition (BNER) has become an important task in biomedical information extraction. In the previous studies based on deep learning, pretrained word embedding becomes an indispensable part of the neural network models, effectively improving their performance. However, the biomedical literature typically contains numerous polysemous and ambiguous words. Using fixed pretrained word representations is not appropriate. Therefore, this paper adopts the pretrained embeddings from language models (ELMo) to generate dynamic word embeddings according to context. In addition, in order to avoid the problem of insufficient training data in specific fields and introduce richer input representations, we propose a multitask learning multichannel bidirectional gated recurrent unit (BiGRU) model. Multiple feature representations (e.g., word-level, contextualized word-level, character-level) are, respectively, or collectively fed into the different channels. Manual participation and feature engineering can be avoided through automatic capturing features in BiGRU. In merge layer, multiple methods are designed to integrate the outputs of multichannel BiGRU. We combine BiGRU with the conditional random field (CRF) to address labels’ dependence in sequence labeling. Moreover, we introduce the auxiliary corpora with same entity types for the main corpora to be evaluated in multitask learning framework, then train our model on these separate corpora and share parameters with each other. Our model obtains promising results on the JNLPBA and NCBI-disease corpora, with F1-scores of 76.0% and 88.7%, respectively. The latter achieves the best performance among reported existing feature-based models.

Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-RoleNets. An IE-RoleNet (interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-RoleNets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization ratio. Hierarchical substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.