Automated Detection of Adverse Drug Reactions from Social Media Posts with Machine Learning

2017 ◽  
pp. 3-15 ◽  
Author(s):  
Ilseyar Alimova ◽  
Elena Tutubalina
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Adverse Drug Reactions ◽  
Automated Detection ◽  
Drug Reactions

scholarly journals Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media

2019 ◽  
Author(s):  
Pilar López Úbeda ◽  
Manuel Carlos Díaz Galiano ◽  
Maite Martin ◽  
L. Alfonso Urena Lopez
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Learning Methods

scholarly journals Normalization of Long-tail Adverse Drug Reactions in Social Media

2020 ◽  
Author(s):  
Emmanouil Manousogiannis ◽  
Sepideh Mesbah ◽  
Alessandro Bozzon ◽  
Robert-Jan Sips ◽  
Zoltan Szlanik ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Long Tail

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 The assessment of similarity vectors of fingerprint and UMLS in adverse drug reaction prediction

2021 ◽  
Author(s):  
Milad Besharatifard ◽  
Zahra Ghorbanali ◽  
Fatemeh Zare-Mirakabad
Keyword(s):  
Machine Learning ◽  
Adverse Drug Reaction ◽  
Adverse Reaction ◽  
Drug Reaction ◽  
Adverse Drug Reactions ◽  
Computational Methods ◽  
Matrix Factorization ◽  
Complex Problem ◽  
Drug Reactions ◽  
Data Set

Identifying and controlling adverse drug reactions is a complex problem in the pharmacological field. Despite the studies done in different laboratory stages, some adverse drug reactions are recognized after being released, such as Rosiglitazone. Due to such experiences, pharmacists are now more interested in using computational methods to predict adverse drug reactions. In computational methods, finding and representing appropriate drug and adverse reaction features are one of the most critical challenges. Here, we assess fingerprint and target as drug features; and phenotype and unified medical language system as adverse reaction features to predict adverse drug reaction. Meanwhile, we show that drug and adverse reaction features represented by similarity vectors can improve adverse drug prediction. In this regard, we propose four frameworks. Two frameworks are based on random forest classification and neural networks as machine learning methods called F_RF and F_NN, respectively. The rest of them improve two state-of-art matrix factorization models, CS and TMF, by considering target as a drug feature and phenotype as an adverse reaction feature. However, machine learning frameworks with fewer drug and adverse reaction features are more accurate than matrix factorization frameworks. In addition, the F_RF framework performs significantly better than F_NN with ACC = %89.15, AUC = %96.14 and AUPRC = %92.9. Next, we contrast F_RF with some well-known models designed based on similarity vectors of drug and adverse reaction features. Unlike other methods, we do not remove rare reactions from the data set in our frameworks. The data and implementation of proposed frameworks are available at http://bioinformatics.aut.ac.ir/ADRP-ML-NMF/.

scholarly journals Predicting adverse drug reaction outcomes with machine learning

2018 ◽  
Vol 5 (3) ◽  
pp. 901 ◽  
Author(s):  
Andy W. Chen
Keyword(s):  
Machine Learning ◽  
Adverse Drug Reactions ◽  
Predictive Models ◽  
Safety Issue ◽  
Adverse Event Reporting System ◽  
Patient Characteristics ◽  
Learning Models ◽  
Drug Reactions ◽  
Drug Safety Issue ◽  
Machine Learning Models

Background: Adverse drug reactions are a drug safety issue affecting more than two million people in the U.S. annually. The Food and Drug Administration (FDA) maintains a comprehensive database of adverse drug reactions reported known as FAERS (FDA adverse event reporting system), providing a valuable resource for studying factors associated with ADRs. The goal of the project is to build predictive models to predict the outcome given patient characteristics and drug usage. The results can be valuable for health care practitioners by offering new knowledge on adverse drug reactions which can be used to improve decision making related to drug prescriptions.Methods: In this paper I present and discuss results from machine learning models used to predict outcomes of ADRs. Machine learning models are a popular set of models for prediction. They have gained attention recently and have been used in a variety of fields. They can be trained on existing data and retrained when new data become available. The trained models are then used to make predictions.Results: I find that the supervised learning models are work similarly within groups, with accuracy between 65% and 75% for predicting deaths and 70% to 75% for predicting hospitalizations. Across groups the models predict hospitalizations better than deaths.Conclusions: The predictive models I built achieve good accuracy. The results can potentially be improved when more data become available in the future.

The role of advanced technologies supplemented with traditional methods in pharmacovigilance sciences

2020 ◽  
Vol 14 ◽  
Author(s):  
M Vijaya Satwika Naidu ◽  
Dudala Sai Sushma ◽  
Varun Jaiswal ◽  
S. Asha ◽  
Tarun Pal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Adverse Drug Reactions ◽  
Mobile Apps ◽  
Health Sector ◽  
Early Developmental Stage ◽  
Drug Reactions ◽  
Traditional Methods ◽  
Advanced Technologies

Background: The immediate automatic systemic monitoring and reporting of adverse drug reaction, improving the efficacy is the utmost need of medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps and on other social websites has increased manifolds requiring data mining techniques. Objective: The objective of this report is to highlight the role of advanced technologies together with traditional methods to proactively aid in early detection of adverse drug reactions concerned with drug safety and pharmacovigilance. Methods: A thorough search was conducted for papers and patents regarding pharmacivigilance. All articles with respect to relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union’s Innovative Medicines Initiative WEB-RADR project emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) playing an important role in timely prediction of risk with high accuracy using two popular approaches the frequentist and Bayesian approach. The pharmacovigilance at premarketing stage is useful for the prediction of the adverse drug reactions in early developmental stage of a drug. Later postmarketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc approaches. Conclusion: The advanced technologies supplemented with traditional technologies is the need of hour for evaluating product’s risk profile and reducing risk in population esp. with comorbid conditions and on concomitant medications.

A Pharmaco-Cybernetics Approach to Patient Safety

2016 ◽  
pp. 1445-1464
Author(s):  
Kevin Yi-Lwern Yap
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Cancer Patients ◽  
Adverse Drug Reactions ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Internet Applications ◽  
Case Examples ◽  
Learning Techniques

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

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