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The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients' clinical records often contain repeated medical information that can significantly impact their health condition. Most existing methods for modeling longitudinal patient information overlook the impact of individual diagnoses and procedures on the patient's health, resulting in insufficient patient representation and limited accuracy of medicine recommendations. Therefore, we propose a medicine recommendation model called KEAN, which is based on an attention aggregation network and enhanced graph convolution. Specifically, KEAN can aggregate individual diagnoses and procedures in patient visits to capture significant features that affect patients' diseases. We further incorporate medicine knowledge from complex medicine combinations, reduce drug-drug interactions (DDIs), and recommend medicines that are beneficial to patients' health. The experimental results on the MIMIC-III dataset demonstrate that our model outperforms existing advanced methods, which highlights the effectiveness of the proposed method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks) applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, embedding model architecture, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output., (©2020 AMIA - All rights reserved.)

Local feature matching has been a critical task in computer vision applications. In the early days of computer vision, local feature matching relied heavily on detector-based methods, where keypoint detectors were used to extract and describe the salient features of an image. However, with the advent of deep learning, detector-free methods that do not rely on keypoint detection have become increasingly popular. These methods directly learn feature descriptors from the image data, leading to improved performance and faster computation times. In this review, we explore the evolution of local feature matching from detector-based to detector-free methods, discussing the advantages and disadvantages of each approach and highlighting the recent advancements in the field. We also discuss the challenges and opportunities for future research in this area. [ABSTRACT FROM AUTHOR]

Drug-target interaction (DTI) is a critical and complex process that plays a vital role in drug discovery and design. In deep learning-based DTI methods, graph neural networks (GNNs) are employed for drug molecule modeling, attention mechanisms are utilized to simulate the interaction between drugs and targets. However, existing methods still face two limitations in these aspects. First, GNN primarily focus on local neighboring nodes, making it difficult to capture the global 3D structure and edge information. Second, the current attention-based methods for modeling drug-target interactions lack interpretability and do not fully utilize the deep representations of drugs and targets. To address the aforementioned issues, we propose an interpretable network architecture called CSDTI. It utilizes a cross-attention mechanism to capture the interaction features between drugs and targets. Meanwhile, we design a drug molecule aggregator to capture high-order dependencies within the drug molecular graph. These features are then utilized simultaneously for downstream tasks. Through rigorous experiments, we have demonstrated that CSDTI outperforms state-of-the-art methods in terms of performance metrics such as AUC, precision, and recall in DTI prediction tasks. Furthermore, the visualization mapping of attention weights indicates that CSDTI can provide chemical insights even without external knowledge. [ABSTRACT FROM AUTHOR]

Named entity recognition is a key step in constructing a knowledge graph in the clock domain. However, the current clock domain has problems such as the small number of labeled samples, which leads to the low accuracy of named entity recognition in the clock domain. To this end, this paper uses the pre-trained language model BERT to extract the features of the text in the clock domain, and then uses the linear chain conditional random field (Linear-CRF) method for sequence labeling, and proposes a BERT-LCRF named entity recognition model. The results of comparative experiments show that the model can fully learn the feature information of the clock domain, improve the accuracy of sequence labeling, and then improve the effect of named entity recognition in the clock domain. [ABSTRACT FROM AUTHOR]

With the development of life science and technology, the literature in the field of biomedicine has grown exponentially. How to excavate and extract valuable information from massive literature has become a new research opportunity in the field of biomedicine. As the core technology of information extraction, named entity recognition and relationship extraction become the basis and key of biomedical text mining. Its main work is to identify the entities in the biomedical text and extract the biomedical semantic relations between the entities. This paper aims to summarize the deep learningbased methods of entity identification and relationship extraction in biomedical field. It comprehensively expounds the development process of various technologies from the perspectives of concept, progress and status quo, and further clarifies the exploration direction of biomedical text information extraction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Large-scale model sizes and expensive computing costs cause the challenge of deploying and applying large pre-trained models. Hence, this paper presents a novel Triple Concepts Attention Mechanism and a lightweight TCAMixer model for edge devices to classify texts. Furthermore, the TCAMixer abstracts textual concepts in a human way, which is unmatched by other counterparts such as pNLP-Mixer (a projection-based MLP-Mixer model for Nature Language Processing) and HyperMixer (a hyper network using dynamic token-mixing layers). Experimental results on several public datasets demonstrate that the TCAMixer outperforms the counterparts by a significant margin, for example, achieving 3% higher accuracy with a smaller model size of 0.177M. Additionally, the TCAMixer achieves a performance of 85% to 98.7% compared to that of large pre-trained models but only occupies 1/3000 to 1/2000 of their size on most test datasets. [ABSTRACT FROM AUTHOR]