DGI Clinical Inc, Halifax, NS, Canada.
Geriatric Medicine Research Unit, Nova Scotia Health Authority, Halifax, NS, Canada.
J Med Internet Res. 2020 Nov 11;22(11):e20840. doi: 10.2196/20840.
SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified.
We aimed to develop a supervised machine learning algorithm to classify dementia stages based on tracked symptoms.
We employed clinical data from 717 people from 3 sources: (1) a memory clinic; (2) long-term care; and (3) an open-label trial of donepezil in vascular and mixed dementia (VASPECT). Symptoms were captured with SymptomGuide Dementia. A clinician classified participants into 4 groups using either the Functional Assessment Staging Test or the Global Deterioration Scale as mild cognitive impairment, mild dementia, moderate dementia, or severe dementia. Individualized symptom profiles from the pooled data were used to train machine learning models to predict dementia severity. Models trained with 6 different machine learning algorithms were compared using nested cross-validation to identify the best performing model. Model performance was assessed using measures of balanced accuracy, precision, recall, Cohen κ, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). The best performing algorithm was used to train a model optimized for balanced accuracy.
The study population was mostly female (424/717, 59.1%), older adults (mean 77.3 years, SD 10.6, range 40-100) with mild to moderate dementia (332/717, 46.3%). Age, duration of symptoms, 37 unique dementia symptoms, and 10 symptom-derived variables were used to distinguish dementia stages. A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance. The correct dementia stage was identified with 83% balanced accuracy (Cohen κ=0.81, AUPRC 0.91, AUROC 0.96). The best performance was seen when classifying severe dementia (AUROC 0.99).
A supervised machine learning algorithm exhibited excellent performance in identifying dementia stages based on dementia symptoms reported in an online environment. This novel dementia staging algorithm can be used to describe dementia stage based on user-reported symptoms. This type of symptom recording offers real-world data that reflect important symptoms in people with dementia.
SymptomGuide 痴呆症（DGI Clinical Inc）是一款可供公众使用的在线症状跟踪工具，旨在为痴呆症患者的护理人员提供支持。当明确特定的痴呆症阶段时，此类数据的价值将得到提升。
我们旨在开发一种基于跟踪症状对痴呆症阶段进行分类的监督机器学习算法。
我们使用了来自三个来源的 717 人的临床数据：（1）记忆诊所；（2）长期护理；（3）血管性和混合性痴呆症（VASPECT）中多奈哌齐的开放性试验。使用 SymptomGuide 痴呆症收集症状。临床医生使用功能评估分期测试或全球衰退量表将参与者分为四组：轻度认知障碍、轻度痴呆、中度痴呆或重度痴呆。使用来自汇总数据的个体化症状特征来训练机器学习模型以预测痴呆症严重程度。使用嵌套交叉验证比较了使用 6 种不同机器学习算法训练的模型，以确定性能最佳的模型。使用平衡准确性、精确性、召回率、Cohen κ、接收器操作特征曲线下的面积（AUROC）和精度-召回曲线下的面积（AUPRC）来评估模型性能。使用优化后的平衡准确性算法来训练最佳表现算法的模型。
研究人群主要为女性（424/717，59.1%），年龄较大（平均 77.3 岁，标准差 10.6，范围 40-100），患有轻度至中度痴呆症（332/717，46.3%）。年龄、症状持续时间、37 种独特的痴呆症状和 10 种症状衍生变量用于区分痴呆症阶段。使用支持向量机机器学习算法使用一对一对比方法训练的模型表现最佳。正确识别痴呆症阶段的准确率为 83%（Cohen κ=0.81，AUPRC 为 0.91，AUROC 为 0.96）。在分类重度痴呆症时，表现最佳（AUROC 为 0.99）。
监督机器学习算法在基于在线环境中报告的痴呆症症状识别痴呆症阶段方面表现出优异的性能。这种新型的痴呆症分期算法可以用于根据用户报告的症状描述痴呆症阶段。这种类型的症状记录提供了反映痴呆症患者重要症状的真实世界数据。
