Drug-drug Interaction Extraction Research Articles

Drug–drug interaction extraction based on multimodal feature fusion by Transformer and BiGRU

Understanding drug–drug interactions (DDIs) plays a vital role in the fields of drug disease treatment, drug development, preventing medical error, and controlling health care-costs. Extracting potential from biomedical corpora is a major complement of existing DDIs. Most existing DDI extraction (DDIE) methods do not consider the graph and structure of drug molecules, which can improve the performance of DDIE. Considering the different advantages of bi-directional gated recurrent units (BiGRU), Transformer, and attention mechanisms in DDIE tasks, a multimodal feature fusion model combining BiGRU and Transformer (BiGGT) is here constructed for DDIE. In BiGGT, the vector embeddings of medical corpora, drug molecule topology graphs, and structure are conducted by Word2vec, Mol2vec, and GCN, respectively. BiGRU and multi-head self-attention (MHSA) are integrated into Transformer to extract the local–global contextual DDIE features, which is important for DDIE. The extensive experiment results on the DDIExtraction 2013 shared task dataset show that the BiGGT-based DDIE method outperforms state-of-the-art DDIE approaches with a precision of 78.22%. BiGGT expands the application of multimodal deep learning in the field of multimodal DDIE.

Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review

Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public health. Therefore, using deep learning technology to extract DDI from scientific literature has become a valuable research direction to researchers. In existing DDI datasets, the number of positive instances is relatively small. This makes it difficult for existing deep learning models to obtain sufficient feature information directly from text data. Therefore, existing deep learning models mainly rely on multiple feature supplementation methods to collect sufficient feature information from different types of data. In this study, the general process of DDI relation extraction based on deep learning is introduced first for comprehensive analysis. Next, we summarize the various feature supplement methods and analyze their merits and demerits. We then review the state-of-the-art literature related to DDI extraction from the deep neural network perspective. Finally, all the feature supplement methods are compared, and some suggestions are given to approach the current problems and future research directions. The purpose of this article is to give researchers a more complete understanding of the feature complementation methods used in DDI extraction to be able to rapidly design and implement custom DDI relation extraction methods.

BBL-GAT: A Novel Method for Drug-Drug Interaction Extraction from Biomedical Literature

BBL-GAT: A Novel Method for Drug-Drug Interaction Extraction from Biomedical Literature

Phar-LSTM: a pharmacological representation-based LSTM network for drug-drug interaction extraction.

Pharmacological drug interactions are among the most common causes of medication errors. Many different methods have been proposed to extract drug-drug interactions from the literature to reduce medication errors over the last few years. However, the performance of these methods can be further improved. In this paper, we present a Pharmacological representation-based Long Short-Term Memory (LSTM) network named Phar-LSTM. In this method, a novel embedding strategy is proposed to extract pharmacological representations from the biomedical literature, and the information related to the target drug is considered. Then, an LSTM-based multi-task learning scheme is introduced to extract features from the different but related tasks according to their corresponding pharmacological representations. Finally, the extracted features are fed to the SoftMax classifier of the corresponding task. Experimental results on the DDIExtraction 2011 and DDIExtraction 2013 corpuses show that the performance of Phar-LSTM is competitive compared with other state-of-the-art methods. Our Python implementation and the corresponding data of Phar-LSTM are available by using the DOI 10.5281/zenodo.8249384.

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug-drug interactions.

During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more "fit for purpose" NLP methods could be developed and used to facilitate the drug development process.

Improving Drug-Drug Interaction Extraction with Gaussian Noise.

Drug-Drug Interactions (DDIs) produce essential and valuable insights for healthcare professionals, since they provide data on the impact of concurrent administration of medications to patients during therapy. In that sense, some relevant works, related to the DDIExtraction2013 Challenge, are available in the current technical literature. This study aims to improve previous results, using two models, where a Gaussian noise layer is added to achieve better DDI relationship extraction. (1) A Piecewise Convolutional Neural Network (PW-CNN) model is used to capture relationships among pharmacological entities described in biomedical databases. Additionally, the model incorporates multichannel words to enrich a person's vocabulary and reduce unfamiliar words. (2) The model uses the pre-trained BERT language model to classify relationships, while also integrating data from the target entities. After identifying the target entities, the model transfers the relevant information through the pre-trained architecture and integrates the encoded data for both entities. The results of the experiment show an improved performance, with respect to previous models.

Drug-Drug Interaction Extraction from Biomedical Text Using Relation BioBERT with BLSTM

In the context of pharmaceuticals, drug-drug interactions (DDIs) occur when two or more drugs interact, potentially altering the intended effects of the drugs and resulting in adverse patient health outcomes. Therefore, it is essential to identify and comprehend these interactions. In recent years, an increasing number of novel compounds have been discovered, resulting in the discovery of numerous new DDIs. There is a need for effective methods to extract and analyze DDIs, as the majority of this information is still predominantly located in biomedical articles and sources. Despite the development of various techniques, accurately predicting DDIs remains a significant challenge. This paper proposes a novel solution to this problem by leveraging the power of Relation BioBERT (R-BioBERT) to detect and classify DDIs and the Bidirectional Long Short-Term Memory (BLSTM) to improve the accuracy of predictions. In addition to determining whether two drugs interact, the proposed method also identifies the specific types of interactions between them. Results show that the use of BLSTM leads to significantly higher F-scores compared to our baseline model, as demonstrated on three well-known DDI extraction datasets that includes SemEval 2013, TAC 2018, and TAC 2019.

MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction

Drug-drug interactions (DDIs) extraction includes identifying drug entities and interactions between drug pairs from the biomedical corpus. The discovery of potential DDIs aids in our understanding of the mechanisms underlying adverse reactions or combination therapy to improve patient safety. The manual extraction of DDIs is very time-consuming and expensive; therefore, computer-aided extraction of DDIs is vital. Many neural network-based methods have been proposed and achieved good efficiency in the extraction of DDIs over the years. However, most studies improved the performance of DDIs extraction with various external drug features while directly using golden drug entities, leading to error propagation and low universality in practical application. In this paper, we propose a new multi-task framework called MTMG, which changes DDIs extraction from a sentence-level classification task to a sequence labeling task named Drug-Specified Token Classification (DSTC). The proposed approach, MTMG, jointly trains DSTC with drug named entity recognition (DNER) and two sentence-level auxiliary tasks we designed. We aim to improve the performance of the entire DDIs extraction pipeline by better using the correlation between entities and relationships and, to the extent possible, using the information of varying granularity implied in the dataset. Experimental results show that MTMG can both improve the accuracy of DNER and DDIs extraction and outperforms state-of-the-art technique.

DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction.

Drug-drug interaction (DDI) may lead to adverse reactions in patients, thus it is important to extract such knowledge from biomedical texts. However, previously proposed approaches typically focus on capturing sentence-aspect information while ignoring valuable knowledge concerning the whole corpus. In this paper, we propose a Multi-aspect Graph-based DDI extraction model, named DDI-MuG. We first employ a bio-specific pre-trained language model to obtain the token contextualized representations. Then we use two graphs to get syntactic information from input instance and word co-occurrence information within the entire corpus, respectively. Finally, we combine the representations of drug entities and verb tokens for the final classification. To validate the effectiveness of the proposed model, we perform extensive experiments on two widely used DDI extraction dataset, DDIExtraction-2013 and TAC 2018. It is encouraging to see that our model outperforms all twelve state-of-the-art models. In contrast to the majority of earlier models that rely on the black-box approach, our model enables visualization of crucial words and their interrelationships by utilizing edge information from two graphs. To the best of our knowledge, this is the first model that explores multi-aspect graphs to the DDI extraction task, and we hope it can establish a foundation for more robust multi-aspect works in the future.

Drug–drug interaction extraction‐based system: An natural language processing approach

AbstractPoly‐medicated patients, especially those over 65, have increased. Multiple drug use and inappropriate prescribing increase drug–drug interactions, adverse drug reactions, morbidity, and mortality. This issue was addressed with recommendation systems. Health professionals have not followed these systems due to their poor alert quality and incomplete databases. Recent research shows a growing interest in using Text Mining via NLP to extract drug–drug interactions from unstructured data sources to support clinical prescribing decisions. NLP text mining and machine learning classifier training for drug relation extraction were used in this process. In this context, the proposed solution allows to develop an extraction system for drug–drug interactions from unstructured data sources. The system produces structured information, which can be inserted into a database that contains information acquired from three different data sources. The architecture outlined for the drug–drug interaction extraction system is capable of receiving unstructured text, identifying drug entities sentence by sentence, and determining whether or not there are interactions between them.

IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction.

Determining drug-drug interactions (DDIs) is an important part of pharmacovigilance and has a vital impact on public health. Compared with drug trials, obtaining DDI information from scientific articles is a faster and lower cost but still a highly credible approach. However, current DDI text extraction methods consider the instances generated from articles to be independent and ignore the potential connections between different instances in the same article or sentence. Effective use of external text data could improve prediction accuracy, but existing methods cannot extract key information from external data accurately and reasonably, resulting in low utilization of external data. In this study, we propose a DDI extraction framework, instance position embedding and key external text for DDI (IK-DDI), which adopts instance position embedding and key external text to extract DDI information. The proposed framework integrates the article-level and sentence-level position information of the instances into the model to strengthen the connections between instances generated from the same article or sentence. Moreover, we introduce a comprehensive similarity-matching method that uses string and word sense similarity to improve the matching accuracy between the target drug and external text. Furthermore, the key sentence search method is used to obtain key information from external data. Therefore, IK-DDI can make full use of the connection between instances and the information contained in external text data to improve the efficiency of DDI extraction. Experimental results show that IK-DDI outperforms existing methods on both macro-averaged and micro-averaged metrics, which suggests our method provides complete framework that can be used to extract relationships between biomedical entities and process external text data.

EMSI-BERT: Asymmetrical Entity-Mask Strategy and Symbol-Insert Structure for Drug–Drug Interaction Extraction Based on BERT

Drug-drug interaction (DDI) extraction has seen growing usage of deep models, but their effectiveness has been restrained by limited domain-labeled data, a weak representation of co-occurring entities, and poor adaptation of downstream tasks. This paper proposes a novel EMSI-BERT method for drug–drug interaction extraction based on an asymmetrical Entity-Mask strategy and a Symbol-Insert structure. Firstly, the EMSI-BERT method utilizes the asymmetrical Entity-Mask strategy to address the weak representation of co-occurring entity information using the drug entity dictionary in the pre-training BERT task. Secondly, the EMSI-BERT method incorporates four symbols to distinguish different entity combinations of the same input sequence and utilizes the Symbol-Insert structure to address the week adaptation of downstream tasks in the fine-tuning stage of DDI classification. The experimental results showed that EMSI-BERT for DDI extraction achieved a 0.82 F1-score on DDI-Extraction 2013, and it improved the performances of the multi-classification task of DDI extraction and the two-classification task of DDI detection. Compared with baseline Basic-BERT, the proposed pre-training BERT with the asymmetrical Entity-Mask strategy could obtain better effects in downstream tasks and effectively limit “Other” samples’ effects. The model visualization results illustrated that EMSI-BERT could extract semantic information at different levels and granularities in a continuous space.

Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature.

Most of the conventional deep neural network-based methods for drug-drug interaction (DDI) extraction consider only context information around drug mentions in the text. However, human experts use heterogeneous background knowledge about drugs to comprehend pharmaceutical papers and extract relationships between drugs. Therefore, we propose a novel method that simultaneously considers various heterogeneous information for DDI extraction from the literature. We first construct drug representations by conducting the link prediction task on a heterogeneous pharmaceutical knowledge graph (KG) dataset. We then effectively combine the text information of input sentences in the corpus and the information on drugs in the heterogeneous KG (HKG) dataset. Finally, we evaluate our DDI extraction method on the DDIExtraction-2013 shared task dataset. In the experiment, integrating heterogeneous drug information significantly improves the DDI extraction performance, and we achieved an F-score of 85.40%, which results in state-of-the-art performance. We evaluated our method on the DrugProt dataset and improved the performance significantly, achieving an F-score of 77.9%. Further analysis showed that each type of node in the HKG contributes to the performance improvement of DDI extraction, indicating the importance of considering multiple pieces of information. Our code is available at https://github.com/tticoin/HKG-DDIE.git.

A Message Passing Approach to Biomedical Relation Classification for Drug–Drug Interactions

The task of extracting drug entities and possible interactions between drug pairings is known as Drug–Drug Interaction (DDI) extraction. Computer-assisted DDI extraction with Machine Learning techniques can help streamline this expensive and time-consuming process during the drug development cycle. Over the years, a variety of both traditional and Neural Network-based techniques for the extraction of DDIs have been proposed. Despite the introduction of several successful strategies, obtaining high classification accuracy is still an area where further progress can be made. In this work, we present a novel Knowledge Graph (KG) based approach that utilizes a unique graph structure in combination with a Transformer-based Language Model and Graph Neural Networks to classify DDIs from biomedical literature. The KG is constructed to model the knowledge of the DDI Extraction 2013 benchmark dataset, without the inclusion of additional external information sources. Each drug pair is classified based on the context of the sentence it was found in, by utilizing transfer knowledge in the form of semantic representations from domain-adapted BioBERT weights that serve as the initial KG states. The proposed approach was evaluated on the DDI classification task of the same dataset and achieved a F1-score of 79.14% on the four positive classes, outperforming the current state-of-the-art approach.

Extracting drug–drug interactions from no-blinding texts using key semantic sentences and GHM loss

The extraction of drug–drug interactions (DDIs) is an important task in the field of biomedical research, which can reduce unexpected health risks during patient treatment. Previous work indicates that methods using external drug information have a much higher performance than those methods not using it. However, the use of external drug information is time-consuming and resource-costly. In this work, we propose a novel method for extracting DDIs which does not use external drug information, but still achieves comparable performance. First, we no longer convert the drug name to standard tokens such as DRUG0, the method commonly used in previous research. Instead, full drug names with drug entity marking are input to BioBERT, allowing us to enhance the selected drug entity pair. Second, we adopt the Key Semantic Sentence approach to emphasize the words closely related to the DDI relation of the selected drug pair. After the above steps, the misclassification of similar instances which are created from the same sentence but corresponding to different pairs of drug entities can be significantly reduced. Then, we employ the Gradient Harmonizing Mechanism (GHM) loss to reduce the weight of mislabeled instances and easy-to-classify instances, both of which can lead to poor performance in DDI extraction. Overall, we demonstrate in this work that it is better not to use drug blinding with BioBERT, and show that GHM performs better than Cross-Entropy loss if the proportion of label noise is less than 30%. The proposed model achieves state-of-the-art results with an F1-score of 84.13% on the DDIExtraction 2013 corpus (a standard English DDI corpus), which fills the performance gap (4%) between methods that rely on and do not rely on external drug information.

IMSE: interaction information attention and molecular structure based drug drug interaction extraction

BackgroundExtraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations.ResultsOur proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets.ConclusionsOur model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions.

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction. In addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 3 0.95) and their relations (F1 3 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.

DREAM: Drug-drug interaction extraction with enhanced dependency graph and attention mechanism.

Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently, graph neural networks are applied on dependency graph to promote the performance of DDI extraction with better semantic representations. However, current method concentrates more on first-order dependency relations and cannot discriminate the connected nodes properly. To better incorporate the dependency relations and improve the representations, we propose a novel DDI extraction method named Drug-drug Interactions extRaction with Enhanced Dependency Graph and Attention Mechanism in this work. Specifically, the dependency graph is enhanced with some potential long-range words to complete the semantic information and fit the aggregation process of graph neural networks. And graph attention mechanism is adopted to further improve word representation by discriminating the connected nodes according to the specific task. Numerical experiments on DDIExtraction 2013 corpus, the benchmark corpus for this domain, demonstrate the superiority of our proposed method.

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.

EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information.

The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules. We evaluated the classification part on 'DDIs 2013' dataset and 'DTIs' dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships. Source code are publicly available at https://github.com/Layne-Huang/EGFI.