Clinical Decision Support Systems to Predict Drug–Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder

Fadwa Alrowais, Saud S. Alotaibi, Anwer Mustafa Hilal (), Radwa Marzouk, Heba Mohsen, Azza Elneil Osman, Amani A. Alneil and Mohamed I. Eldesouki
Additional contact information
Fadwa Alrowais: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Saud S. Alotaibi: Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Makkah 24211, Saudi Arabia
Anwer Mustafa Hilal: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
Radwa Marzouk: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Heba Mohsen: Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
Azza Elneil Osman: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
Amani A. Alneil: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
Mohamed I. Eldesouki: Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia

IJERPH, 2023, vol. 20, issue 3, 1-15

Abstract: Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug–drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.

Keywords: healthcare; decision making; big data; drug–drug interaction; deep learning; predictive models (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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