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CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aided CTG technology, Intelligent CTG classification based on FHR is a fundamental component of these technologies. Its implementation can provide doctors with auxiliary decisions. Most of existing FHR classification methods are based on combing different deep learning models, such as CNN (Convolutional Neural Network), LSTM (Long short-term memory) and Transformer. However, these studies ignore the balance of positive and negative samples in dataset and the matching degree between model and FHR classification task, which reduces the classification accuracy. In this paper, we mainly discuss two major problems in previous FHR classification studies: reduce class imbalance and select appropriate convolution kernel. To address above two problems, we propose a data augmentation method based on ECMN (Edge Clipping and Multiscale Noise) to resolve class imbalance. Subsequently, we introduce a one-dimensional long convolutional layer, which use trend area to calculate the appropriate convolution kernel. Based on appropriate convolution kernel, an improved residual structure with attention mechanism named TGLCN (Trend-Guided Long Convolution Network) is proposed to improve FHR classification accuracy. Finally, horizontal and longitudinal experiments show that the TGLCN obtains high classification accuracy and speed of parameter adjustment., 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 © 2023 Elsevier Ltd. All rights reserved.)

• We develop a clinically interpretable dual-stream AI model for the automatic diagnosis of fetal distress. • We combine clinical knowledge traction to quantize numerous clinical diagnostic indicators of authoritative guidelines. • We employ the TreeSHAP-based white box interpreter and CNN-based case tracking model to enhance clinical interpretability. • Our approach achieves a high precision pathological diagnosis of 0.963, which provides solutions for future AI-aided diagnosis systems. Clinically, Fetal Heart Rate (FHR)-based intelligent cardiotocography classification to diagnose fetal well-being is of utmost importance for obstetricians and gynecologists. However, the current Fetal Distress Diagnosis (FDD) algorithms based on Artificial Intelligence (AI) methods are designed as black box models that usually result in poor performance on clinical interpretation and lack explicit diagnostic evidence. This paper focuses on two aspects: enhancing the accuracy of FD and providing explanations for the model's decisions. Specifically, we propose a novel clinically interpretable dual-stream AI architecture for FDD. The two streams embed multi-feature category representations and the original FHR into the Digital Twin model (DT) and the deep convolutional neural Networks-based Case Tracking model (CTNet), respectively, thus marking as DT-CTNet. And the former makes the input-outcome dependency transparent, while the latter provides the samples that are the most similar to the object being diagnosed. By this design, not only is high-precision FDD achievable, but a transparent explanation with global, local and instantiation interpretation can be obtained. To the best of our knowledge, this work is the first to provide an interpretable diagnosis of fetal distress. The proposed method is comprehensively evaluated from five aspects: classification model accuracy, explanation of the classification model decision, consistency of the evidence explanation, pathological difference in visual analysis, and a comparison with state-of-the-art methods. Experimental results, which are performed on a challenging dataset, show that the proposed fetal distress diagnosis method outperforms the state-of-the-art methods and has a high accuracy (0.963). [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel FHR classification model, called DATF, is proposed for fetal state assessment detection. • The DTAF model combines the global and local trends of time series to strengthen the connection between the information. • The Big Trend and Small Trend reduce the degree of feature information and effectively connect discrete points to show more information. • Introducing attention mechanism at the end of the DTAF model, keeping more global view of output. Fetal heart rate monitoring based on fetal heart rate (FHR) is an important diagnostic tool for evaluating fetal health in late pregnancy. Currently, intelligent cardiotocography (CTG) classification based on FHR signals is a challenging area of research that can assist doctors in making clinical decisions to improve the efficiency and accuracy of pregnancy management. Most of the existing methods focus on converting FHR signals into image signals or classifying them based on the discreteness of the original signal without consideration of long-term characteristics and continuity of the signal, which causes scattered and incomplete signal feature extraction. This paper focuses on two major bottlenecks of deep learning algorithms for FHR time series classification: continuity of the FHR signal, and long-term features of the FHR signals. In order to solve these two problems, we propose a position encoding method called Double Trend Accumulation (DTA) through establish the big and small trends to extract the long-term features of FHR signals. In addition, we introduce timestamp scaling and value scaling to fusion the two long-term features after extraction of big trend and small trend. Based on DTA, we also design an improved model named Double Trend Accumulation Former (DTAF), which uses the structure of attention mechanism to classify the FHR signals. Finally, the ablation experiments, comparison experiments, and resilience experiments are used to verify the superiority and validity of the DTA method. [ABSTRACT FROM AUTHOR]

Rating-based methods (e.g., collaborative filtering) in recommendation can explicitly model users and items from their rating patterns, nevertheless suffer from the natural data sparsity problem. In other hand, user-generated reviews can provide rich semantic information of user preference and item features, and can alleviate the sparsity problems of rating data. In fact, ratings and reviews are complementary and can be viewed as two different sides of users and items, hence fusing rating patterns and text reviews effectively has the potential to learn more accurate representations of users and items for recommendation. In this paper, we propose a hybrid neural recommendation model to learn the deep representations for users and items from both ratings and reviews. Our model contains three major components, i.e., a rating-based encoder to learn deep and explicit features from rating patterns of users and items, a review-based encoder to model users and items from text reviews, and the prediction module for recommendation according to the rating- and review-based representations of users and items. In addition, considering that different reviews have different informativeness for modelling users and items, we introduce a novel review-level attention mechanism incorporating with rating-based representation as query vector to select useful reviews. We conduct extensive experiments on several benchmark datasets and the experimental results demonstrate that our model can outperform the existing competitive baseline methods in recommendations. [ABSTRACT FROM AUTHOR]

Role-based network embedding aims to map network into low-dimensional node representations while preserving structural similarities. Adjacency matrix contain both the local and global information of a network, but it cannot directly represent the role of nodes. So it is essential to extract higher-order structural features from adjacency matrix for role-based network embedding (structural equivalence). While being sensitive to noise in real networks in general, the features extracted by some identical methods cannot truly represent the role of nodes. Therefore, we propose a deep learning framework RESD. In detail, we first propose extracting higher-order structural features for each node in the network. Then, we utilize the Variational Auto-Encoder (VAE) to model the nonlinear relationship of the features, reduce noise and improve the robustness of embedding. Furthermore, in the embedding space, we apply a degree-regularized constraint to guide the representation learning for preserving key structural information of nodes (e.g., degrees), which may be lost due to the principle of VAE framework. Finally, we construct a unified objective function to learn the node embedding for role discovery by preserving the structural features and node degree. We compare our model with several state-of-the-art methods on real-world networks. The results of extensive experiments demonstrate the effectiveness of our model and prove that our model scales well with dimension and network size. [ABSTRACT FROM AUTHOR]