摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-关于重度抑郁症的新研究是一篇报道的主题。根据中国人民共和国杭州的新闻报道，NewsRx记者的研究表明：“缺乏临床有效的生物标志物或客观方案阻碍了抑郁症患者(MDD)的有效诊断。与健康对照(HC)相比，抑郁症患者血浆蛋白水平和神经影像学表现出现异常。”本研究以100例MDD和100例HC的血液样本以及46例MDD和49例HC的MRI图像为分析对象，提出了一种新的算法，将图形神经网络和注意力模块相结合，将神经网络与注意力模块相结合。将该算法应用于包含上述蛋白质定量和神经图像的数据集中，评价将神经图像整合到模型中是否能提高模型的性能，并与HC进行比较。MDD在MRI显示的血浆蛋白水平和灰质体积上有显著的差异。新算法表现出了优越的性能,F1值和准确率分别达到0.9436%和94.08%。神经影像学数据的整合增强了新算法的性能,提高了F1值和准确率。这项样本量小的单中心研究需要在更大的测试集上进行评估，以提高可靠性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Major Depressive Disorder is the subject of a report. According to news reporting from Hangzhou, People's Republi c of China, by NewsRx journalists, research stated, "The absence of clinically-v alidated biomarkers or objective protocols hinders effective major depressive di sorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presentations." The news correspondents obtained a quote from the research, "Despite extensive m achine learning studies in psychiatric diagnosis, a reliable tool integrating mu lti-modality data is still lacking. In this study, blood samples from 100 MDD an d 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we de vised a novel algorithm, integrating graph neural networks and attention modules , for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and O rexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithm s. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance. Compared to HC, MDD showed significant alte rations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm's performance, resulting in an improved F1 value a nd accuracy, reaching 0.9543 and 95.06 %. This single-center study with a small sample size requires future evaluations on a larger test set for im proved reliability."
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