Motivation
Because unanticipated drug-drug interactions (DDIs) can result in severe bodily harm, identifying the adverse effects of polypharmacy is one of the most important tasks in human health. Over the past few decades, computational methods for predicting the adverse effects of polypharmacy have been developed.


Results
This article presents DPSP, a framework for predicting polypharmacy side effects based on the construction of novel drug features and the application of a deep neural network to predict DDIs. In the first step, a variety of drug information is evaluated, and a feature extraction method and the Jaccard similarity are used to determine similarities between two drugs. By combining these similarities, a novel feature vector is generated for each drug. In the second step, the method predicts DDIs for specific DDI events using a multimodal framework and drug feature vectors. On three benchmark datasets, the performance of DPSP is measured by comparing its results to those of several well-known methods, such as GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, DNN, DeepDDI, KNN, LR, and RF. DPSP outperforms these classification methods based on a variety of classification metrics. The results indicate that the use of diverse drug information is effective and efficient for identifying DDI adverse effects.


Availability and implementation
The source code and datasets are available at https://github.com/raziyehmasumshah/DPSP.

DPSP: a multimodal deep learning framework for polypharmacy side effects prediction.

Predicting the onset of cardiovascular disease (CVD) has been a hot topic for researchers for years, and recently, the concept of transfer learning has been gaining traction in this field. Transfer learning (TL) is a process that involves transferring information gained from one task or domain to another related task or domain. This paper comprehensively reviews recent advancements in pre-trained TL models for CVD, focusing on electrocardiogram (ECG) signals. Forty-three articles were chosen from Scopus and Google Scholar sources and reviewed, focusing on the type of CVD detected, the database used, the ECG input format, and the pre-training model used for transfer learning. The results show that more than 80% of the studies utilize 2-dimensional (2D) ECG input from the two most utilized available ECG datasets: MIT-BIH arrhythmia (ARR) and MIT-BIH normal sinus rhythm. alexnet, visual geometry group (VGG), and residual network (ResNet) are among the pre-trained TL models with the highest number used among reviewed articles. Additionally, the development of pre-trained TL models over time has made it possible to detect CVD with ECG signals. It can also address limited data problems, promote the development of more dependable and resilient detection systems, and aid medical professionals in diagnosing CVD and other diseases. 

Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models

Improving automated labeling with deep learning and signal segmentation for accurate ECG signal analysis

As a primary indicator for cardiovascular diseases, the electrocardiogram (ECG) is commonly used for arrhythmia classification. Many related studies emphasize that the R-peaks of the ECG signal are essential for extracting features or signal segmentation. Thus, the chosen R-peak detection algorithm affects classification performance. Furthermore, the lack of distinct R-peaks in arrhythmias like ventricular flutter makes these rhythms difficult to identify, regardless of the detection algorithm. Therefore, this study proposes a novel ECG rhythm classification framework that does not depend on R-peak detection. First, the n number of beat segments is acquired by sliding a window over the ECG signal. A scalogram is then produced from each segment and fed into a pre-trained beat classifier to generate n beat score vectors. These vectors are concatenated chronologically to establish an n-beat score map (n-BSM), which serves as input for our rhythm classification model. The n-BSM of a rhythm conveys information regarding its constituent n beats by sequentially arranging their characteristics, each captured by a score distribution over various beat types. Experimental results from the MIT-BIH arrhythmia database (MITDB) demonstrate that the proposed method improves overall performance in classifying ten ECG rhythms. Moreover, we achieved a 97.56% F1 score for a ventricular flutter rhythm lacking distinct R-peaks. We also utilized the MIT-BIH Malignant Ventricular Ectopy (VFDB) and the Chapman-Shaoxing 12-lead ECG databases (SPH) to verify the proposed method’s robustness and generalizability. The average accuracy for different rhythms was 99.28% and 88.83%, respectively. 

Using beat score maps with successive segmentation for ECG classification without R-peak detection

A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve these issues, we proposed a novel method that distinguishes a given ECG rhythm using a beat score map (BSM) image. Through the proposed method, the associations between beats and previously used features, such as the R–R interval, were considered. Rhythm classification was implemented by training a convolutional neural network model and using transfer learning with the created BSM image. As a result, the proposed method for ECG rhythms with small data samples showed significant results. It also showed good performance in differentiating atrial fibrillation (AFIB) and atrial flutter (AFL) rhythms, which are difficult to distinguish due to their similar characteristics. The performance for rhythms with a small number of samples of the proposed method is 20% better than an existing method. In addition, the performance based on the F-1 score for classifying AFIB and AFL of the proposed method is 30% better than the existing method. This study solved the previous limitations caused by small sample numbers and similar rhythms.

/pdf/method-for-solving-difficulties-in-rhythm-classification-12yp9vqy.pdf

Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms

Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data may hinder improvement of performance. Moreover, compared with all possible drug combinations, the highly limited range of drug combinations used for model training prevents achieving high generalizability. To handle these problems, we propose a unified embedding-based prediction model using knowledge graph constructed with data of drug–protein and protein–protein interactions. Herein, single or multiple drugs or proteins are mapped into the same embedding space, allowing us to (1) jointly utilize side effect occurrence data associated with single drugs and multidrug combinations to train prediction models and (2) quantify connectivity strengths between drugs and other entities such as proteins. Due to these characteristics, it becomes also possible to utilize the quantified relationships between distant nodes, as well as neighboring nodes, of all possible multidrug combinations to regularize the models. Compared with existing methods, our model showed improved performance, especially in predicting the side effects of new combinations containing novel drugs that have no clinical information on polypharmacy effects. Furthermore, our unified embedding vectors have been shown to provide interpretability, albeit to a limited extent, for proteins highly associated with multidrug side effect.

/pdf/a-knowledge-graph-embedding-approach-for-polypharmacy-side-1emd7fc9.pdf

A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction

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Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms

A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction