Orimaye Sylvester O, Wong Jojo S-M, Golden Karen J, Wong Chee P, Soyiri Ireneous N
Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
BMC Bioinformatics. 2017 Jan 14;18(1):34. doi: 10.1186/s12859-016-1456-0.
The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.
Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).
Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.
对诸如阿尔茨海默病（AD）及相关痴呆症等神经退行性疾病进行人工诊断一直是一项挑战。目前，这些疾病是通过特定的临床诊断标准和神经心理学检查来诊断的。使用多种机器学习算法，利用言语发声产生的低级语言特征构建自动诊断模型，有助于从大量人群中诊断出可能患有AD的患者。为此，我们在痴呆症银行语言转录临床数据集上开发了不同的机器学习模型，该数据集由99名可能患有AD的患者和99名健康对照组成。
我们的模型学习了多种句法、词汇和n元语法语言生物标志物，以区分可能患有AD的组和健康组。与健康组相比，我们发现可能患有AD的患者在其语言中句法成分的使用显著减少，词汇成分的使用显著增加。此外，我们观察到在n元语法的使用上存在显著差异，因为健康组能够在其n元语法中识别和理解比可能患有AD的组更多的对象。因此，我们最好的诊断模型使用支持向量机（SVM），通过更好的接受操作特征曲线下面积（AUC），显著区分了可能患有AD的组和健康老年组。
实验和统计评估表明，使用机器学习算法从老年人的言语发声中学习语言生物标志物，有助于对可能患有AD的患者进行临床诊断。我们强调，预测疾病组的最佳机器学习模型结合了重要的句法、词汇和前n元语法特征。然而，需要在更大的数据集上训练诊断模型，这可能会带来更好的AUC，并对可能患有AD的患者进行更好的临床诊断。
