PDF(Pubmed)
Abstract：
The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study\'s objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual\'s conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.
摘要：
识别和管理神经系统疾病的过程面临挑战，促使新方法的研究，以提高诊断的准确性。在这项研究中,我们进行了系统的文献综述,以确定基于遗传和分子途径的机器学习(ML)模型在治疗神经系统疾病中的意义.根据研究的目标，开发了搜索策略，以使用数字图书馆提取研究。我们遵循严格的研究选择标准。共有24项研究符合纳入标准并被纳入审查。我们根据神经系统疾病对研究进行了分类。纳入的研究强调了治疗神经系统疾病的多种方法和出色的结果。研究结果强调了现有模型的潜力，根据个人情况提出个性化干预措施。这些发现提供了性能更好的方法，可以处理遗传学和分子数据以产生有效的结果。此外，我们讨论了未来的研究方向和挑战，强调在现实世界的临床环境中推广现有模型的需求。这项研究有助于提高神经系统疾病诊断和管理领域的知识。
