Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease.
Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease.
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DOI：
10.1038/s41531-022-00409-5
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发表时间：
2022-11-07
影响因子：
8.7
通讯作者：
Pishva, Ehsan
中科院分区：
文献类型：
--
作者：
Harvey, Joshua;Reijnders, Rick A.;Cavill, Rachel;Duits, Annelien;Kohler, Sebastian;Eijssen, Lars;Rutten, Bart P. F.;Shireby, Gemma;Torkamani, Ali;Creese, Byron;Leentjens, Albert F. G.;Lunnon, Katie;Pishva, Ehsan
认知障碍是帕金森氏病(PD)的一种衰弱症状。我们的目标是建立一个准确的多变量机器学习(ML)模型，以根据帕金森进展标志物倡议(PPMI)预测新诊断的PD患者的认知结果。为期8年的年度认知评估被用来定义(I)认知障碍和(Ii)痴呆症转化的两种认知结果。选定的基线变量被组织成临床、生物体液和遗传/表观遗传指标的三个子集，并使用四种不同的ML算法进行测试。不考虑使用的ML算法，由临床变量组成的模型执行得最好，并且显示出更好地预测痴呆症转化后认知障碍的结果。我们观察到，当临床、生物体液和表观遗传/遗传变量都包括在一个模型中时，预测性能略有改善。当包括临床变量时，几项脑脊液指标和一个表观遗传学标记物在多个模型中显示出高预测性权重。
Cognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.
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影响因子：
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