This review aims to systematically assess the current status and prospects of
artificial intelligence (AI) in the rehabilitation management of patients with
schizophrenia and their impact on the rehabilitation process. We selected 70
studies from 2012 to the present, focusing on application, technology
categories, products, and data types of machine learning, deep learning,
reinforcement learning, and other technologies in mental health interventions
and management. The results indicate that AI can be widely used in symptom
monitoring, relapse risk prediction, and rehabilitation treatment by analyzing
ecological momentary assessment, behavioral, and speech data. This review
further explores the potential challenges and future directions of emerging
products, technologies, and analytical methods based on AI, such as social
media analysis, serious games, and large language models in rehabilitation. In
summary, this study systematically reviews the application status of AI in
schizophrenia rehabilitation management and provides valuable insights and
recommendations for future research paths.

该研究系统评估了人工智能在精神分裂症康复管理中的应用现状和前景以及其对康复过程的影响，并提供了有价值的见解和未来研究方向的建议。

人工智能在精神分裂症康复管理中的应用：系统文献综述

Application of Artificial Intelligence in Schizophrenia Rehabilitation  Management: Systematic Literature Review

Parkinson's disease (PD) is a neurodegenerative disease with frequently
changing motor symptoms where continuous symptom monitoring enables more
targeted treatment. Classical time series classification (TSC) and deep
learning techniques have limited performance for PD symptom monitoring using
wearable accelerometer data because PD movement patterns are complex, but
datasets are small. We investigate InceptionTime and RandOm Convolutional
KErnel Transform (ROCKET) because they are state-of-the-art for TSC and
promising for PD symptom monitoring: InceptionTime's high learning capacity is
suited to modeling complex movement patterns while ROCKET is suited to small
datasets. We used a random search to find the highest-scoring InceptionTime
architecture and compared it to ROCKET with a ridge classifier and a
multi-layer perceptron (MLP) on wrist motions of PD patients. We find that all
approaches are suitable for estimating tremor severity and bradykinesia
presence but struggle with detecting dyskinesia. ROCKET performs better for
dyskinesia, whereas InceptionTime is slightly better for tremor and
bradykinesia but has much higher variability in performance. Both outperform
the MLP. In conclusion, both InceptionTime and ROCKET are suitable for
continuous symptom monitoring, with the choice depending on the symptom of
interest and desired robustness.

介绍了使用 InceptionTime 和 RandOm Convolutional KErnel Transform (ROCKET) 处理可穿戴加速度计数据在帕金森病患者的症状监测方面的研究，结果显示这两种方法都适用于连续症状监测，但选择取决于所关注的症状和期望的稳健性。