Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning

In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditional Random Field (BILSTM-CRF). From the sentences in the literature data, a dependency graph was constructed which discovered the relations between gene and drug. The product of the drug on phenotype rule was identified by the Gene Regulation Score (GRS) which creates the drug-topic probability matrix. The probability matrix and a syntactic distance measure was processed in Classification and Regression Tree (CART), Naïve Bayes (NB), logistic regression and Convolutional Neural Network (CNN) classifiers for estimating the drug-gene and drug-side effects. Besides the literature data, social media offers various promising resources with massive volume of data that can be useful in the drug-phenotype and drug-side effect association prediction. So in this paper, drug information with gene, disease and side effects are extracted from different social media such as Twitter, Facebook and LinkedIn and it can be used with the literature data to provide more relevant disease and drug relations. In addition to this, topic modeling with transfer learning is introduced to consider the element categories, probability of overlapping elements and deep contextual significance of a text for better modeling of topics. The topic modeling with transfer learning shares as much knowledge as possible between the literature data and social media information for topic modeling. The topics from social media and literature data are used for creating the drug-topic matrix. The probability matrix and syntactic distance measure are given as input to CART, NB, logistic regression and CNN for estimating the drug-gene and drug-side effect association. This proposed work is named as Enhanced Topic Modeling with Transfer Leaning- IPISTON (ETPTL-IPISTON). The simulation findings exhibit that the efficiency of ETPTL-IPISTON than the traditional methods.

aer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations

Adverse event report (AER) data are a key source of signal for post marketing drug surveillance. The standard methodology to analyze AER data applies disproportionality metrics, which estimate the strength of drug/side-effect associations from discrete counts of their occurrence at report level. However, in other domains, improvements in predictive modeling accuracy have been obtained through representation learning, where discrete features are replaced by distributed representations learned from unlabeled data. This paper describes aer2vec, a novel representational approach for AER data in which concept embeddings emerge from neural networks trained to predict drug/side-effect co-occurrence. Trained models are evaluated for their utility in identifying drug/side-effect relationships, with improvements over disproportionality metrics in most cases. In addition, we evaluate the utility of an otherwise-untapped resource in the Food and Drug Administration (FDA) AER system – reporter designations of suspected causality – and find that incorporating this information enhances performance of all models evaluated.

The Impact of Pharmaceutical Side Effects on Audiological and Vestibular Measurements

There are over 2,000 drugs with a combined total of over 400 side effects that could result in obtaining inaccurate case history information or inaccurate test results that may lead to misdiagnosing the patient's hearing or vestibular problem. The recommendations that are made could be inappropriate and thus can lead to management errors. A review of the auditory, vestibular, and cognitive side effects of many of the drugs patients take regularly (including drugs that can cause tinnitus) is provided. This article offers suggestions to obtain a more accurate case history. A review of preferred Web sites to obtain drug side effect information is included. Suggestions for improved communication strategies between the audiologist, the physician, the patient, and the pharmacist are highlighted.