Efficient methods of analysis readily available for clinicians continue to be limited within neuropsychological assessment at the raw data level. Here, a novel approach for generating predictive response patterns and analysing neuropsychological raw data is offered. In order to assess the usefulness of association rule learning as an analysis tool for neuropsychological raw data, Frequent Pattern Growth (FP-Growth) was used to mine patterns from the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-NB) database. Complete assessment data for 84 post-mortem confirmed Alzheimer’s disease (AD) cases and 294 age, race, and education matched controls were analysed across baseline and one-year follow-up using FP-Growth, for the purpose of assessing the clinical utility of a finer analysis at the raw data level and the feasibility of predicting response patterns for clinical/control groups. Output from FP-Growth, in terms of the number of frequent itemsets retained across both evaluation timepoints, was discernable between controls, mild, and moderate to severe Alzheimer’s disease cases (p < .001, and η² = .488). Patterns within raw data scores, both in terms of frequent itemsets and predictive association rules, appear to be differentiable across groups within neuropsychological analysis, which indicates that FP-Growth is applicable as a supplementary analysis tool for neuropsychological assessment by means of offering an additional level of data analysis, predictive item response capabilities, and aiding in clinical decision making.

中文翻译：

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阿尔茨海默病神经心理学数据分析中的关联规则学习

在原始数据级别的神经心理学评估中，临床医生容易获得的有效分析方法继续受到限制。在这里，提供了一种用于生成预测反应模式和分析神经心理学原始数据的新方法。为了评估关联规则学习作为神经心理学原始数据分析工具的有用性，频繁模式增长 (FP-Growth) 用于从联盟中挖掘模式以建立阿尔茨海默病神经心理电池 (CERAD-NB) 数据库的注册表. 使用 FP-Growth 分析了 84 例死后确诊的阿尔茨海默病 (AD) 病例和 294 名年龄、种族和教育匹配的对照的完整评估数据，在基线和一年的随访中进行了分析，目的是评估在原始数据级别进行更精细分析的临床效用以及预测临床/对照组反应模式的可行性。FP-Growth 的输出，就在两个评估时间点保留的频繁项集的数量而言，在对照组、轻度和中度至重度阿尔茨海默病病例之间是可辨别的。p  < .001，并且 η ²  = .488）。原始数据得分中的模式，无论是在频繁项集还是预测关联规则方面，在神经心理学分析中似乎在各组之间是可区分的，这表明 FP-Growth 通过提供额外的水平可用作神经心理学评估的补充分析工具数据分析、预测项目响应能力和帮助临床决策。