Chinese word embedding is a significant task in natural language processing (NLP). Most researchers explored Chinese word embedding according to radical, component, stroke n -gram and character features. Besides these features, Chinese characters still have structure and pinyin characteristics. In this article, we propose ensemble ssp2vec and connective ssp2vec to utilize inner-character features (stroke, structure, and pinyin) for learning Chinese word embeddings. Then we design hierarchical ssp2vec to forecast the contexts according to the combination of inner-character (stroke, structure, and pinyin) and inner-word features (character) of Chinese words to explore different feature combination ways for learning feature relevance and comprehending word semantics, where feature substring is proposed to learn the relevancy of stroke, structure, and pinyin. Experimental results for word analogy, word similarity, text classification, and named entity recognition tasks demonstrate that the proposed methods outperform most state-of-the-art models.

Identification of Alzheimer's disease (AD) with multimodal neuroimaging data has been receiving increasing attention. However, the presence of numerous redundant features and corrupted neuroimages within multimodal datasets poses significant challenges for existing methods. In this paper, we propose a feature selection method named Enhanced Multimodal Low-rank Embedding (EMLE) for multimodal AD diagnosis. Unlike previous methods utilizing convex relaxations of the -norm, EMLE exploits an -norm regularized projection matrix to obtain an embedding representation and select informative features jointly for each modality. The -norm, employing an upper-bounded nonconvex Minimax Concave Penalty (MCP) function to characterize sparsity, offers a superior approximation for the -norm compared to other convex relaxations. Next, a similarity graph is learned based on the self-expressiveness property to increase the robustness to corrupted data. As the approximation coefficient vectors of samples from the same class should be highly correlated, an MCP function introduced norm, i.e., matrix -norm, is applied to constrain the rank of the graph. Furthermore, recognizing that diverse modalities should share an underlying structure related to AD, we establish a consensus graph for all modalities to unveil intrinsic structures across multiple modalities. Finally, we fuse the embedding representations of all modalities into the label space to incorporate supervisory information. The results of extensive experiments on the Alzheimer's Disease Neuroimaging Initiative datasets verify the discriminability of the features selected by EMLE.

Abstract Medication combination recommendation is critical in clinic, since accurately predicting therapeutic drug can provide essential decision support to physicians. However, current approaches do not consider the multilevel structure of electronic health record (EHR) data or the hierarchical dependencies between multiple visits, leading to suboptimal recommendations. To address these limitations, we propose a novel hierarchical feedback interaction network (HIFINet) to utilize an examination-diagnosis-treatment hierarchical network for modeling the inherent multilevel structure of EHR data. The feedback long short-term memory network called FeLSTM, which is the basic unit of our hierarchical network, performs hierarchical interactions and leverages change information as feedback to propagate forward among different levels. Additionally, HIFINet contains four modules. First, an embedding module is designed to learn the health information representation of patients. Second, a three-layer time-series learning module is employed to capture temporal dependencies within each sequence. Next, a differential feedback interaction module is developed to capture the difference features between visits. Finally, an attention fusion module is used to learn a comprehensive representation of the patient’s health information and to recommend next multiple treatment medications. HIFINet is compared with state-of-the-art approaches on a real-world dataset. The results indicate that HIFINet outperforms other approaches, offering more accurate recommendations.

In Alzheimer’s disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples’ transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with ℓ2,1-norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified Multi-Attribute Discriminative Representation Learning (MADRL) model for ADDI prediction. Unlike the existing works that equally treat features of each attribute without discrimination and do not consider the underlying relationship among drugs, we first develop a regularized optimization problem based on CUR matrix decomposition for joint representative drug and discriminative feature selection such that the selected drugs and features can well approximate the original feature spaces and the critical factors discriminative to ADDIs can be properly explored. Different from the existing models that ignore the consistent and unique properties among attributes, a Generative Adversarial Network (GAN) framework is then designed to capture the inter-attribute shared and intra-attribute specific representations of adverse drug pairs for exploiting their consensus and complementary information in ADDI prediction. Meanwhile, MADRL is compatible with any kind of attributes and capable of exploring their respective effects on ADDI prediction. An iterative algorithm based on the alternating direction method of multipliers is developed for optimization. Experiments on publicly available dataset demonstrate the effectiveness of MADRL when compared with eleven baselines and its six variants.

Adverse drug–drug interaction (ADDI) causes harmful injuries and accidental deaths in patients, posing as a significant life-threatening issue in public health. Early prediction of ADDIs has become an increasingly concerning task for the safety of pharmacotherapy during clinical treatments. In this article, we propose an adaptive regularized multiattribute fuzzy distance (MAFD) learning model for ADDI prediction. Unlike the existing works that only focus on whether an adverse interaction occurs or not for a specific drug pair and do not consider their implicit medication risks, MAFD employs fuzzy distance learning by designing a fuzzy membership matrix to model the adverse distance with a fuzziness level for exploring the medication risks of adverse drug pairs. Meanwhile, for each attribute, we develop two projection matrices to respectively map its original feature and adverse interaction spaces into a common space for eliminating noisy information and capturing their compact and informative representations. Besides, adaptive regularization is explicitly designed to investigate the underlying characteristics of different attributes in ADDI modeling and neighborhood structure preservation is seamlessly integrated to benefit the prediction results. The optimization problem is solved by an iterative algorithm based on the alternating direction method of multipliers with detailed convergence proofs. Experiments on real-world dataset demonstrate the effectiveness of MAFD when compared with ten baselines and its five variants.

Chinese word embedding models capture Chinese semantics based on the character feature of Chinese words and the internal features of Chinese characters such as radical, component, stroke, structure and pinyin. However, some features are overlapping and most methods do not consider their relevance. Meanwhile, they express words as point vectors that cannot better capture different aspect semantics of Chinese words. In this paper, we propose a Feature Subsequence based Probability Representation Model (FSPRM) for learning Chinese word embeddings, in which we first integrate the morphological and phonetic features (stroke, structure and pinyin) of Chinese characters and learn their relevance by designing a feature subsequence to capture relatively comprehensive semantics of Chinese words, then feature probability distribution is proposed for capturing different aspect meanings of Chinese words based on the three internal features and probability representation by estimating its mean as the sum of feature subsequences. Chinese words with similar features may have similar semantics, then we map Chinese words to feature probability distributions and design a similarity-based objective for predicting the contextual words of the target word to learn their semantics. Extensive experiments on word analogy, word similarity, text classification and named entity recognition tasks demonstrate that the proposed method outperforms most state-of-the-art approaches.

Adverse drug-drug interaction (ADDI) becomes a significant threat to public health. Despite the detection of ADDIs is experimentally implemented in the early development phase of drug design, many potential ADDIs are still clinically explored by accidents, leading to a large number of morbidity and mortality. Several computational models are designed for ADDI prediction. However, they take no consideration of drug dependency, although many drugs usually produce synergistic effects and own highly mutual dependency in treatments, which contains underlying information about ADDIs and benefits ADDI prediction. In this paper, we design a dependent network to model the drug dependency and propose an attribute supervised learning model Probabilistic Dependent Matrix Tri-Factorization (PDMTF) for ADDI prediction. In particular, PDMTF incorporates two drug attributes, molecular structure and side effect, and their correlation to model the adverse interactions among drugs. The dependent network is represented by a dependent matrix, which is first formulated by the row precision matrix of the predicted attribute matrices and then regularized by the molecular structure similarities among drugs. Meanwhile, an efficient alternating algorithm is designed for solving the optimization problem of PDMTF. Experiments demonstrate the superior performance of the proposed model when compared with eight baselines and its two variants.

Chinese word embeddings have recently attracted much attention in natural language processing (NLP). Existing researches learn Chinese word embeddings based on characters, radicals, components and stroke n-gram. Besides abovementioned features, Chinese characters also own structure and pinyin features. In this paper, we design feature substring, a super set of radicals, components and stroke n-gram with structure and pinyin information, to integrate stroke, structure and pinyin features of Chinese characters and capture the semantics of Chinese words. Based on the feature substring, we propose a novel method ssp2vec to predict the contextual words based on the feature substrings of the target words for learning Chinese word embeddings. It is based on our observation that exploiting the morphological information (stroke and structure) and the phonetic information (pinyin) is crucial for capturing the meanings of Chinese words. Meanwhile, the phonetic information (pinyin) can assist the model to distinguish Chinese words. Experimental results on word analogy, word similarity, text classification and named entity recognition tasks show that the proposed method obtains better results than state-of-the-art approaches.