Contemporary large language models (LLMs) may have utility for processing
unstructured, narrative free-text clinical data contained in electronic health
records (EHRs) -- a particularly important use-case for mental health where a
majority of routinely-collected patient data lacks structured, machine-readable
content.
A significant problem for the the United Kingdom's National Health Service
(NHS) are the long waiting lists for specialist mental healthcare. According to
NHS data, in each month of 2023, there were between 370,000 and 470,000
individual new referrals into secondary mental healthcare services. Referrals
must be triaged by clinicians, using clinical information contained in the
patient's EHR to arrive at a decision about the most appropriate mental
healthcare team to assess and potentially treat these patients.
The ability to efficiently recommend a relevant team by ingesting potentially
voluminous clinical notes could help services both reduce referral waiting
times and with the right technology, improve the evidence available to justify
triage decisions.
We present and evaluate three different approaches for LLM-based, end-to-end
ingestion of variable-length clinical EHR data to assist clinicians when
triaging referrals. Our model is able to deliver triage recommendations
consistent with existing clinical practices and it's architecture was
implemented on a single GPU, making it practical for implementation in
resource-limited NHS environments where private implementations of LLM
technology will be necessary to ensure confidential clinical data is
appropriately controlled and governed.

通过解决长期等待次名单和提供相关团队推荐，基于大型语言模型的端到端入食方法有助于缩短患者延迟时间和改善三级分类决策，适用于精神医疗保健。

面向精神健康护理的定制大型语言模型用于数字分诊辅助

Bespoke Large Language Models for Digital Triage Assistance in Mental  Health Care

In recent years the NHS has been having increased difficulty seeing all
low-risk patients, this includes but not limited to suspected osteoarthritis
(OA) patients. To help address the increased waiting lists and shortages of
staff, we propose a novel method of automated biomarker identification for
diagnosis of knee disorders and the monitoring of treatment progression. The
proposed method allows for the measurement and analysis of biomechanics and
analyse their clinical significance, in both a cheap and sensitive alternative
to the currently available commercial alternatives. These methods and results
validate the capabilities of standard RGB cameras in clinical environments to
capture motion and show that when compared to alternatives such as depth
cameras there is a comparable accuracy in the clinical environment. Biomarker
identification using Principal Component Analysis (PCA) allows the reduction of
the dimensionality to produce the most representative features from motion
data, these new biomarkers can then be used to assess the success of treatment
and track the progress of rehabilitation. This was validated by applying these
techniques on a case study utilising the exploratory use of local anaesthetic
applied on knee pain, this allows these new representative biomarkers to be
validated as statistically significant (p-value < 0.05).

提出了一种用于诊断膝关节疾病和监测治疗进展的自动生物标志物识别方法，该方法使用标准 RGB 相机以廉价且敏感的方式测量和分析生物力学，利用主成分分析技术进行生物标志物识别和降维，并应用于本地麻醉用于膝痛的病例，验证了其在临床环境中的有效性和统计学意义 (p 值 < 0.05)。