Table_6_Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.docx

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup.Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools.Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases.Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

引言：随着研究焦点转向针对阿尔茨海默病（AD）的个性化医疗，迫切需要能够利用诊断生物标志物量化患者风险的工具。医学信息学平台（MIP）是一种分布式电子基础设施，通过整合大量数据并结合机器学习（ML）算法与统计模型，以定义疾病的生物特征。本研究评估了（i）MIP中实现的两种机器学习算法，即监督式梯度提升（GB）和半无监督的3C策略（分类、聚类、分类—CCC）的准确性；（ii）它们对标准诊断流程的贡献。方法：我们研究了来自意大利3个记忆诊所的MIP安装点的人群，包括正常认知（CN，n = 432）、轻度认知障碍（MCI，n = 456）和AD（n = 451）的受试者。GB分类器被应用于在1,339名受试者中最佳地区分三种诊断类别，而CCC策略则用于细化经典疾病分类。四位痴呆症专家对38名独立队列患者的MCI转化诊断信心（DC）进行了评估。DC基于临床、神经心理学、CSF和结构MRI信息，并在MIP工具的结果基础上再次进行评估。结果：GB算法在CN与MCI、AD的嵌套10折交叉验证中提供了85%的分类准确性。在保留验证中，准确性提高至95%，每次轮流排除每个意大利临床队列。CCC确定了五组同质受试者群和36个代表疾病特征的生物标志物。在DC评估中，CCC在MCI人群中定义了六个聚类以训练算法，并使用了29个生物标志物以改善患者分期。GB和CCC显示出显著的影响，评估为医生DC的+5.99%的增量。在80%的情况下，MIP对DC的影响被评为从“轻微”到“显著”。讨论：GB在CN、MCI和AD的分类中提供了公正的结果。CCC通过其半监督方法确定了同质且具有前景的受试者类别。我们测量了MIP对医生DC的影响。我们的结果为建立一种新的机器学习区分患者是否会转化为AD的范式铺平了道路，这对于神经学而言是一个临床上的优先事项。