首页|University of Pavia Reports Findings in Machine Learning (Unraveling sex differe nces in Parkinson's disease through explainable machine learning)
University of Pavia Reports Findings in Machine Learning (Unraveling sex differe nces in Parkinson's disease through explainable machine learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Pavia, Italy, by NewsR x correspondents, research stated, “Sex differences affect Parkinson’s disease ( PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions.” Our news journalists obtained a quote from the research from the University of P avia, “Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significa nt for predicting PD, despite its score. Interactions between features require c onsideration, as do distinctions between scoring disparities and actual feature importance. For instance, a higher score in males for a certain feature doesn’t necessarily mean it’s less important for characterizing PD in females. This arti cle proposes an explainable Machine Learning (ML) model to elucidate these under lying factors, emphasizing the importance of features. This insight could be cri tical for personalized medicine, suggesting the need to tailor data collection a nd analysis for males and females. The model identifies sex-specific differences in PD, aiding in predicting outcomes as ‘Healthy’ or ‘Pathological’. It adopts a system-level approach, integrating heterogeneous data - clinical, imaging, gen etics, and demographics - to study new biomarkers for diagnosis. The explainable ML approach aids non- ML experts in understanding model decisions, fostering tru st and facilitating interpretation of complex ML outcomes, thus enhancing usabil ity and translational research. The ML model identifies muscle rigidity, autonom ic and cognitive assessments, and family history as key contributors to PD diagn osis, with sex differences noted. The genetic variant SNCA-rs356181 may be more significant in characterizing PD in males. Interaction analysis reveals a greate r occurrence of feature interplay among males compared to females.”
NETL
NSTL
