Gielis Karsten, Vanden Abeele Marie-Elena, Verbert Katrien, Tournoy Jos, De Vos Maarten, Vanden Abeele Vero
e-Media Research Lab, KU Leuven, Leuven, Belgium.
Memory Clinic, Jessa Hospital, Hasselt, Belgium.
Digit Biomark. 2021 Feb 19;5(1):44-52. doi: 10.1159/000514105. eCollection 2021 Jan-Apr.
Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this follow-up is conducted at separate moments in time with large intervals in between. Casual games, embedded into the daily life of older adults, may prove to be a less resource-intensive medium that yields continuous and rich data on a patient's cognition.
To explore whether digital biomarkers of cognitive performance, found in the casual card game Klondike Solitaire, can be used to train machine-learning models to discern games played by older adults living with MCI from their healthy counterparts.
Digital biomarkers of cognitive performance were captured from 23 healthy older adults and 23 older adults living with MCI, each playing 3 games of Solitaire with 3 different deck shuffles. These 3 deck shuffles were identical for each participant. Using a supervised stratified, 5-fold, cross-validated, machine-learning procedure, 19 different models were trained and optimized for F1 score.
The 3 best performing models, an Extra Trees model, a Gradient Boosting model, and a Nu-Support Vector Model, had a cross-validated F1 training score on the validation set of ≥0.792. The F1 score and AUC of the test set were, respectively, >0.811 and >0.877 for each of these models. These results indicate psychometric properties comparative to common cognitive screening tests.
The results suggest that commercial card games, not developed to address specific mental processes, may be used for measuring cognition. The digital biomarkers derived from Klondike Solitaire show promise and may prove useful to fill the current blind spot between consultations.
轻度认知障碍（MCI）是一种认知功能出现轻微但明显衰退的状况，这种衰退超过了正常的年龄相关变化。患有MCI的老年人发展为痴呆症的几率更高，这就需要在记忆诊所进行定期的认知随访。然而，由于时间和资源的限制，这种随访是在不同的时间点进行的，中间间隔时间较长。融入老年人日常生活的休闲游戏，可能是一种资源消耗较少的媒介，能够产生关于患者认知的持续且丰富的数据。
探讨在休闲纸牌游戏《克朗代克纸牌接龙》中发现的认知表现数字生物标志物，是否可用于训练机器学习模型，以区分患有MCI的老年人和健康老年人所玩的游戏。
从23名健康老年人和23名患有MCI的老年人中获取认知表现的数字生物标志物，每位参与者用3种不同的牌组洗牌方式各玩3局纸牌接龙游戏。这3种牌组洗牌方式对每位参与者都是相同的。使用有监督的分层5折交叉验证机器学习程序，针对F1分数训练和优化了19种不同的模型。
表现最佳的3个模型，即极端随机树模型、梯度提升模型和Nu支持向量模型，在验证集上的交叉验证F1训练分数≥0.792。每个模型的测试集F1分数和AUC分别>0.811和>0.877。这些结果表明其心理测量特性与常见的认知筛查测试相当。
结果表明，并非为解决特定心理过程而开发的商业纸牌游戏，可能可用于测量认知。从《克朗代克纸牌接龙》中得出的数字生物标志物显示出前景，可能有助于填补目前两次会诊之间的空白。
