Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach  

作　　者：Ernesto Lee Furqan Rustam Hina Fatima Shahzad Patrick Bernard Washington Abid Ishaq Imran Ashraf 

机构地区：[1]Department of Computer Science,Broward College,Broward Count,Florida,USA [2]Department of Computer Science,Khwaja Fareed University of Engineering and Information Technology,Rahim Yar Khan,64200,Pakistan [3]Division of Business Administration and Economics,Morehouse College,Atlanta,GA,USA [4]Department of Information and Communication Engineering,Yeungnam University,Gyeongsan-si,38541,Korea

出　　处：《Computer Systems Science & Engineering》2023年第6期3053-3077,共25页计算机系统科学与工程（英文）

摘　　要：With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large poten-tial for providing valuable insights into these cases.Therefore,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘Drugs.Com’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)network.The CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based methods.Performance analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical sig-nificance T-tes

关 键 词：Drug safety analysis lexicon-based techniques drug reviews sentiment analysis machine learning CNN-LSTM 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]