scholarly journals Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sajid Hussain ◽  
Hammad Afzal ◽  
Ramsha Saeed ◽  
Naima Iltaf ◽  
Mir Yasir Umair
Keyword(s):  
Adverse Drug Reactions ◽  
Text Classification ◽  
Pharmacological Action ◽  
Fine Tuning ◽  
Testing Time ◽  
Drug Reactions ◽  
Training Time ◽  
Proposed Model ◽  
End To End ◽  
The Given

Adverse drug reactions (ADRs) are the undesirable effects associated with the use of a drug due to some pharmacological action of the drug. During the last few years, social media has become a popular platform where people discuss their health problems and, therefore, has become a popular source to share information related to ADR in the natural language. This paper presents an end-to-end system for modelling ADR detection from the given text by fine-tuning BERT with a highly modular Framework for Adapting Representation Models (FARM). BERT overcame the predominant neural networks bringing remarkable performance gains. However, training BERT is a computationally expensive task which limits its usage for production environments and makes it difficult to determine the most important hyperparameters for the downstream task. Furthermore, developing an end-to-end ADR extraction system comprising two downstream tasks, i.e., text classification for filtering text containing ADRs and extracting ADR mentions from the classified text, is also challenging. The framework used in this work, FARM-BERT, provides support for multitask learning by combining multiple prediction heads which makes training of the end-to-end systems easier and computationally faster. In the proposed model, one prediction head is used for text classification and the other is used for ADR sequence labeling. Experiments are performed on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets. The proposed model is compared with the baseline models and state-of-the-art techniques, and it is shown that it yields better results for the given task with the F -scores of 89.6%, 97.6%, 84.9%, and 95.9% on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets, respectively. Moreover, training time and testing time of the proposed model are compared with BERT’s, and it is shown that the proposed model is computationally faster than BERT.

scholarly journals Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11774
Author(s):  
Hannah McConnell ◽  
T. Daniel Andrews ◽  
Matt A. Field
Keyword(s):  
Adverse Drug Reactions ◽  
Pharmacological Action ◽  
Underlying Mechanism ◽  
Sequence Conservation ◽  
Target Type ◽  
Type B ◽  
Drug Reactions ◽  
Target Drug ◽  
Functional Inference ◽  
Target Effects

Background Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools—these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. Methods Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. Results As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as ‘benign’. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as ‘benign’. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. Conclusion In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.

scholarly journals Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases

2021 ◽  
Vol 1 ◽  
Author(s):  
Attayeb Mohsen ◽  
Lokesh P. Tripathi ◽  
Kenji Mizuguchi
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Adverse Drug Reactions ◽  
Predictive Models ◽  
Prediction Models ◽  
Expression Profiles ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Wide Range

Machine learning techniques are being increasingly used in the analysis of clinical and omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) and the build-up of health-related big data. In this paper we have aimed at estimating the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery using various machine learning methods. We have also described a novel machine learning-based framework for predicting the likelihood of ADRs. Our framework combines two distinct datasets, drug-induced gene expression profiles from Open TG–GATEs (Toxicogenomics Project–Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs. It incorporates data filtering and cleaning as well as feature selection and hyperparameters fine tuning. Using this framework with Deep Neural Networks (DNN), we built a total of 14 predictive models with a mean validation accuracy of 89.4%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As case studies, we have investigated the performances of our prediction models in the context of Duodenal ulcer and Hepatitis fulminant, highlighting mechanistic insights into those ADRs. We have generated predictive models to help to assess the likelihood of ADRs in testing novel pharmaceutical compounds. We believe that our findings offer a promising approach for ADR prediction and will be useful for researchers in drug discovery.

scholarly journals Clinical view on adverse drug reactions, pharmacovigilance in India and role of clinical pharmacist

2021 ◽  
Vol 10 (3) ◽  
pp. 042-047
Author(s):  
Sughosh Vishweshwar Upasani ◽  
Manali Sughosh Upasani ◽  
Ansari Imtiyaz Ahmed Tufail Ahmed ◽  
Nilesh Subhashchandra Jain ◽  
Punam Rajendra Pal
Keyword(s):  
Adverse Drug Reactions ◽  
Health Care Services ◽  
Adverse Reactions ◽  
Pharmacological Action ◽  
Drug Reactions ◽  
Pharmacovigilance Activity ◽  
Unsuccessful Attempt ◽  
Care Services ◽  
Controller General

An Adverse Drug Reaction (ADRs) is still a challenge in modern healthcare, increasing complication of therapeutics, an elderly populace and increasing multimorbidity. Pharmacovigilance is the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine related problem. This article is having objective of evaluating the pharmacist perception about Pharmacovigilance and ADRs monitoring through ample literature review. In India pharmacovigilance activity begins in 1986 with ADR monitoring system under supervision of drug controller general of India. The prescribed National Pharmacovigilance Program was commence in 2005; with unsuccessful attempt in 1998, and renamed as Pharmacovigilance Program of India (PvPI) in 2010. Adverse drug reactions monitoring has become an essential part to be executed together with other health-care services for a safe use of medicines. Pharmacist can play an important role in evaluation of ADRs. Pharmacist – drug expert- having abundant knowledge of pharmacological action, pharmaco-therapeutics, adverse reactions, and disease pathophysiology, can make the drug therapy safer.

scholarly journals Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor

2020 ◽  
Vol 10 (13) ◽  
pp. 4474 ◽  
Author(s):  
Direselign Addis Tadesse ◽  
Chuan-Ming Liu ◽  
Van-Dai Ta
Keyword(s):  
Region Of Interest ◽  
Input Image ◽  
Text Detection ◽  
Fine Tuning ◽  
Proposed Model ◽  
Scene Text ◽  
Feature Extractor ◽  
Reading Text ◽  
Text Reading ◽  
End To End

Reading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the frequency of input images at feature extraction, which slows down the system, requires more memory, and recognizes text inaccurately. In this paper, we proposed an octave convolution (OctConv) feature extractor and a time-restricted attention encoder-decoder module for end-to-end scene text reading. The OctConv can extract features by factorizing the input image based on their frequency. It is a direct replacement of convolutions, orthogonal and complementary, for reducing redundancies and helps to boost the reading text through low memory requirements at a faster speed. In the text reading process, features are first extracted from the input image using Feature Pyramid Network (FPN) with OctConv Residual Network with depth 50 (ResNet50). Then, a Region Proposal Network (RPN) is applied to predict the location of the text area by using extracted features. Finally, a time-restricted attention encoder-decoder module is applied after the Region of Interest (RoI) pooling is performed. A bilingual real and synthetic scene text dataset is prepared for training and testing the proposed model. Additionally, well-known datasets including ICDAR2013, ICDAR2015, and Total Text are used for fine-tuning and evaluating its performance with previously proposed state-of-the-art methods. The proposed model shows promising results on both regular and irregular or curved text detection and reading tasks.

An Ensembled SVM Based Approach For Predicting Adverse Drug Reactions

2020 ◽  
Vol 15 ◽  
Author(s):  
Pratik Joshi ◽  
V Masilamani ◽  
Raj Ramesh
Keyword(s):  
Adverse Drug Reactions ◽  
Prediction Models ◽  
Poor Performance ◽  
Data Sets ◽  
Drug Reactions ◽  
Average Sensitivity ◽  
Data Set ◽  
The People ◽  
Optimal Kernel ◽  
The Given

Background: Preventing adverse drug reactions (ADRs) is imperative for the safety of the people. The problem of under-reporting the ADRs has been prevalent across the world, making it difficult to develop the prediction models, which are unbiased. As a result, most of the models are skewed to the negative samples leading to high accuracy but poor performance in other metrics such as precision, recall, F1 score, and AUROC score. Objective: In this work, we have proposed a novel way of predicting the ADRs by balancing the dataset. Method: The whole data set has been partitioned into balanced smaller data sets. SVMs with optimal kernel have been learned using each of the balanced data sets and the prediction of given ADR for the given drug has been obtained by voting from the ensembled optimal SVMs learned. Results: We have found that results are encouraging and comparable with the competing methods in the literature and obtained the average sensitivity of 0.97 for all the ADRs. The model has been interpreted and explained with SHAP values by various plots.

scholarly journals Quantifying the severity of adverse drug reactions using social media

2021 ◽  
Author(s):  
Adam Lavertu ◽  
Tymor Hamamsy ◽  
Russ B Altman
Keyword(s):  
Adverse Drug Reactions ◽  
The United States ◽  
Boxed Warning ◽  
Label Propagation ◽  
Drug Reactions ◽  
Drug Label ◽  
Quantitative Estimates ◽  
Drug Risk ◽  
Initial Seed ◽  
The Given

AbstractAdverse drug reactions (ADRs) impact the health of 100,000s of individuals annually in the United States with associated costs in the hundreds of billions. The monitoring and analysis of the severity of adverse drug reactions is limited by the current qualitative and categorical system of severity classifications. Previous efforts have generated quantitative estimates for a subset of ADRs, but were limited in scope due to the time and costs associated with the efforts. We present a semi-supervised approach that estimates ADR severity by using a lexical network of ADR word embeddings and label propagation. We use this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from MedDRA. Our Severity of Adverse Events Derived from Reddit (Saedr) scores have good correlations with real-world outcomes. Saedr scores had Spearman correlations with ADR case outcomes in FAERS of 0.595, 0.633, and −0.748 for death, serious outcome, and no outcome, respectively. We investigate different methods for defining initial seed term sets and evaluate their impact on severity estimates. We analyzed severity distributions for ADRs based on their appearance in Boxed Warning drug label sections, as well as ADRs with sex-specific associations. We find that ADRs discovered postmarket have significantly greater severity compared to those discovered in the clinical trial. We create quantitative Drug RIsk Profile (Drip) scores for 968 drugs that have a Spearman correlation of 0.377 with drugs ranked by FAERS cases resulting in death, where the given drug was the primary suspect. We make the Saedr and Drip scores publicly available in order to enable more quantitative analysis of pharmacovigilance data.

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