首页|Data on Major Depressive Disorder Reported by Bo Lin and Colleagues (Graph convo lutional network with attention mechanism improve major depressive depression di agnosis based on plasma biomarkers and neuroimaging data)
Data on Major Depressive Disorder Reported by Bo Lin and Colleagues (Graph convo lutional network with attention mechanism improve major depressive depression di agnosis based on plasma biomarkers and neuroimaging data)
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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."
HangzhouPeople's Republic of ChinaAs iaAlgorithmsBiomarkersBloodCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineHematologyMachine LearningMajor Depressiv e DisorderNeuroimagingPlasma
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