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

Background: (Importance) Cerebrovascular accident (Stroke) is a term used in medicine to describe the process of 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 among the symptoms of this condition. (Objective) This paper analyzes available studies on Cerebrovascular accident medication combinations.Method: (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 ۲۰۲۲. A network meta-analysis examines the efficacy of this drug combination on genes/proteins that serve as progression targets for cerebrovascular accident. Results: In scenarios ۱ through ۳, the p-values for the suggested medication combination and Cerebrovascular accident were ۰.۰۳۶۶۳۳, ۰.۰۰۷۷۶۳, and ۰.۰۰۳۶۳۸, respectively. Note that scenario I is the combination of medications initially indicated for the treatment of a cerebrovascular accident. The recommended combination of medications for cerebrovascular accidents is ۱۰ times more effective. Conclusion: This systematic review and network meta-analysis demonstrates that the recommended medication combination decreases the p-value between cerebrovascular accident and the genes as potential progression targets, thereby enhancing the treatment for cerebrovascular accident. The optimal combination of medications improves community health and decreases per-person management costs.

کلیدواژه ها:

Genes ، Biological data ، Drug Combination ، Network Meta-analysis ، Reinforcement Learning ، Cerebrovascular accident ، Stroke