Recommending Drug Combinations using Reinforcement Learning to target Genes/proteins that cause Stroke: A comprehensive Systematic Review and Network Meta-analysis

Abstract

ABSTRACTObjectives(Importance)Cerebrovascular accident (Stroke) is a term used in medicine to describe cutting off blood supply to a portion of the brain, which causes tissue damage in the brain. Clots of blood that form in the brain’s blood vessels and ruptures in the brain’s blood vessels are the root causes of cerebrovascular accidents. Dizziness, numbness, weakness on one side of the body, and difficulties communicating verbally, writing, or comprehending language are the symptoms of this condition. Smoking, being older and having high blood pressure, diabetes, high cholesterol, heart disease, a history of cerebrovascular accident in the family, atherosclerosis (which is the buildup of fatty material and plaque inside the coronary arteries), or high cholesterol all contribute to an increased risk of having a cerebrovascular accident.(Objective)This paper analyzes available studies on Cerebrovascular accident medication combinations.Evidence acquisition(Data sources)This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. A network meta-analysis examines the efficacy of this drug combination on genes/proteins that serve as progression targets for cerebrovascular accidents.Results and ConclusionIn scenarios 1 through 3, the p-values for the suggested medication combination and Cerebrovascular accident were 0.036633, 0.007763, and 0.003638, respectively. Scenario I is the combination of medications initially indicated for treating a cerebrovascular accident. The recommended combination of medications for cerebrovascular accidents is ten times more effective. This systematic review and network meta-analysis demonstrate that the recommended medication combination decreases the p-value between cerebrovascular accidents and the genes as potential progression targets, thereby enhancing the treatment for cerebrovascular accidents. The optimal combination of medications improves community health and decreases per-person management costs.HighlightsCombined drugs that make the p-value between Stroke and target genes close to 1Using Reinforcement Learning to recommend drug combinationA comprehensive systematic review of recent worksA Network meta-analysis to measure the comparative efficacyConsidered drug interactions