An extensive library of symptom inventories has been developed over time to
measure clinical symptoms, but this variety has led to several long standing
issues. Most notably, results drawn from different settings and studies are not
comparable, which limits reproducibility. Here, we present an artificial
intelligence (AI) approach using semantic textual similarity (STS) to link
symptoms and scores across previously incongruous symptom inventories. We
tested the ability of four pre-trained STS models to screen thousands of
symptom description pairs for related content - a challenging task typically
requiring expert panels. Models were tasked to predict symptom severity across
four different inventories for 6,607 participants drawn from 16 international
data sources. The STS approach achieved 74.8% accuracy across five tasks,
outperforming other models tested. This work suggests that incorporating
contextual, semantic information can assist expert decision-making processes,
yielding gains for both general and disease-specific clinical assessment.

通过使用语义相似度的人工智能方法，将症状和得分联系起来，从而解决了不同研究环境和研究结果不可比较的问题，提高了症状评估的准确性和临床决策过程中的效率。

利用语义文本相似度关联症状清单

Linking Symptom Inventories using Semantic Textual Similarity

Neurodegeneration characterizes individuals with different dementia subtypes
(e.g., individuals with Alzheimer's Disease, Primary Progressive Aphasia, and
Parkinson's Disease), leading to progressive decline in cognitive, linguistic,
and social functioning. Speech and language impairments are early symptoms in
individuals with focal forms of neurodegenerative conditions, coupled with
deficits in cognitive, social, and behavioral domains. This paper reviews the
findings on language and communication deficits and identifies the effects of
dementia on the production and perception of discourse. It discusses findings
concerning (i) language function, cognitive representation, and impairment,
(ii) communicative competence, emotions, empathy, and theory-of-mind, and (iii)
speech-in-interaction. It argues that clinical discourse analysis can provide a
comprehensive assessment of language and communication skills in individuals,
which complements the existing neurolinguistic evaluation for (differential)
diagnosis, prognosis, and treatment efficacy evaluation.

本篇论文回顾了有关语言和沟通缺陷的发现，并分析了认知、社交、行为各方面的影响。文章探讨临床话语分析法，它可以为病人的语言和沟通技能提供全面评估，可以补充现有的神经语言学评估，用于诊断、预测和评估治疗的有效性。

痴呆症患者的话语和对话障碍

Discourse and conversation impairments in patients with dementia

This paper presents a novel deep learning enabled, video based analysis
framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS)
that can be used in the clinic or at home. We report results from comparing the
performance of the framework to that of trained clinicians on a population of
32 Parkinsons disease (PD) patients. In-person clinical assessments by trained
neurologists are used as the ground truth for training our framework and for
comparing the performance. We find that the standard sit-to-stand activity can
be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture
instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75
using our framework compared to 0.50 for the video based rater clinicians,
while for PIGD we find 0.78 for the framework and 0.45 for the video based
rater clinicians. We believe our proposed framework has potential to provide
clinically acceptable end points of PD in greater granularity without imposing
burdens on patients and clinicians, which empowers a variety of use cases such
as passive tracking of PD progression in spaces such as nursing homes, in-home
self-assessment, and enhanced tele-medicine.

本文提出了一种基于视频的深度学习分析框架，用于评估统一帕金森病评定量表 (UPDRS) 的方法，并与训练有素的临床医生的表现进行比较，结果发现该框架可用于评估 BRADY 和 PIGD 的子得分，并具有潜力提供更丰富的 PD 患者的临床记录，从而可以在本地或在医生不在的情况下进行 PD 的监测与评估。