ki:elements GmbH, Saarbrücken, Germany.
Methods Mol Biol. 2024;2785:299-309. doi: 10.1007/978-1-0716-3774-6_18.
Digital biomarkers are of growing interest in the field of Alzheimer's Disease (AD) research. Digital biomarker data arising from digital health tools holds various potential benefits: more objective and more accurate assessment of patients' symptoms and remote collection of signals in real-world scenarios but also multimodal variance for prediction models of individual disease progression. Speech can be collected at minimal patient burden and provides rich data for assessing multiple aspects of AD pathology including cognition. However, the operations around collecting, preparing, and validly interpreting speech data within the context of clinical research on AD remains complex and sometimes challenging. Through a dedicated pipeline of speech collection tools, preprocessing steps and algorithms, precise qualification and quantification of an AD patient's pathology can be achieved from their speech. The aim of this chapter is to describe the methods that are needed to create speech collection scenarios that result in valuable speech-based digital biomarkers for clinical research.
数字生物标志物在阿尔茨海默病（AD）研究领域越来越受到关注。来自数字健康工具的数字生物标志物数据具有多种潜在的好处：更客观、更准确地评估患者的症状，并在现实场景中远程收集信号，但也为个体疾病进展的预测模型提供了多模态方差。语音可以以最小的患者负担采集，并为评估 AD 病理的多个方面提供丰富的数据，包括认知。然而，在 AD 临床研究背景下收集、准备和有效解释语音数据的操作仍然复杂，有时甚至具有挑战性。通过专门的语音采集工具、预处理步骤和算法管道，可以从患者的语音中精确地定性和定量分析 AD 患者的病理。本章的目的是描述创建语音采集场景的方法，这些场景可为临床研究产生有价值的基于语音的数字生物标志物。
