This research report presents a proof-of-concept study on the application of
machine learning techniques to video and speech data collected during
diagnostic cognitive assessments of children with a neurodevelopmental
disorder. The study utilised a dataset of 39 video recordings, capturing
extensive sessions where clinicians administered, among other things, four
cognitive assessment tests. From the first 40 minutes of each clinical session,
covering the administration of the Wechsler Intelligence Scale for Children
(WISC-V), we extracted head positions and speech turns of both clinician and
child. Despite the limited sample size and heterogeneous recording styles, the
analysis successfully extracted path signatures as features from the recorded
data, focusing on patient-clinician interactions. Importantly, these features
quantify the interpersonal dynamics of the assessment process (dialogue and
movement patterns). Results suggest that these features exhibit promising
potential for predicting all cognitive tests scores of the entire session
length and for prototyping a predictive model as a clinical decision support
tool. Overall, this proof of concept demonstrates the feasibility of leveraging
machine learning techniques for clinical video and speech data analysis in
order to potentially enhance the efficiency of cognitive assessments for
neurodevelopmental disorders in children.

这项研究报告提出了在诊断认知发展障碍的儿童的视频和语音数据中应用机器学习技术的概念验证研究。该研究利用了 39 个视频录制的数据集，收集了包括四项认知评估测试在内的临床会诊的广泛内容。通过从每个临床会诊的前 40 分钟中提取临床医生和儿童的头部位置和语音转换，尽管样本数量有限且录制风格不同，分析成功地从记录的数据中提取了路径特征，重点关注患者和医生之间的互动。结果表明，这些特征有望用于预测整个会诊过程中所有认知测试的分数，以及用作临床决策支持工具的预测模型的原型。总体而言，这个概念验证证明了利用机器学习技术进行临床视频和语音数据分析的可行性，以潜在地提高儿童神经发育障碍的认知评估的效率。

患者 - 临床医师互动的路径签名表征作为儿童神经心理测试结果的预测器：概念验证

Path Signature Representation of Patient-Clinician Interactions as a  Predictor for Neuropsychological Tests Outcomes in Children: A Proof of  Concept

Conversational agents (CAs) have the great potential in mitigating the
clinicians' burden in screening for neurocognitive disorders among older
adults. It is important, therefore, to develop CAs that can be engaging, to
elicit conversational speech input from older adult participants for supporting
assessment of cognitive abilities. As an initial step, this paper presents
research in developing the backchanneling ability in CAs in the form of a
verbal response to engage the speaker. We analyzed 246 conversations of
cognitive assessments between older adults and human assessors, and derived the
categories of reactive backchannels (e.g. "hmm") and proactive backchannels
(e.g. "please keep going"). This is used in the development of TalkTive, a CA
which can predict both timing and form of backchanneling during cognitive
assessments. The study then invited 36 older adult participants to evaluate the
backchanneling feature. Results show that proactive backchanneling is more
appreciated by participants than reactive backchanneling.

该研究开发了一种名为 TalkTive 的交互式智能助手，通过 backchanneling 技术帮助老年人进行神经认知障碍的评估，研究结果表明主动的 backchanneling 反馈比被动的反馈更受老年人欢迎。