Diabetic eye disease is a major cause of blindness worldwide. The ability to
monitor relevant clinical trajectories and detect lapses in care is critical to
managing the disease and preventing blindness. Alas, much of the information
necessary to support these goals is found only in the free text of the
electronic medical record. To fill this information gap, we introduce a system
for extracting evidence from clinical text of 19 clinical concepts related to
diabetic eye disease and inferring relevant attributes for each. In developing
this ophthalmology phenotyping system, we are also afforded a unique
opportunity to evaluate the effectiveness of clinical language models at
adapting to new clinical domains. Across multiple training paradigms, we find
that BERT language models pretrained on out-of-distribution clinical data offer
no significant improvement over BERT language models pretrained on non-clinical
data for our domain. Our study tempers recent claims that language models
pretrained on clinical data are necessary for clinical NLP tasks and highlights
the importance of not treating clinical language data as a single homogeneous
domain.

糖尿病性眼病是全球失明的主要原因之一。从糖尿病性眼病的临床文本中提取证据，推断相关属性，以监测疾病发展并避免失明，这些任务并不需要基于临床数据预训练的语言模型，强调了不将临床语言数据作为单一的同质领域的重要性。

关注临床 BERT：研究语言模型在糖尿病视网膜病变表型识别中的推广性能

An Eye on Clinical BERT: Investigating Language Model Generalization for  Diabetic Eye Disease Phenotyping

Clinical natural language processing requires methods that can address
domain-specific challenges, such as complex medical terminology and clinical
contexts. Recently, large language models (LLMs) have shown promise in this
domain. Yet, their direct deployment can lead to privacy issues and are
constrained by resources. To address this challenge, we delve into synthetic
clinical text generation using LLMs for clinical NLP tasks. We propose an
innovative, resource-efficient approach, ClinGen, which infuses knowledge into
the process. Our model involves clinical knowledge extraction and
context-informed LLM prompting. Both clinical topics and writing styles are
drawn from external domain-specific knowledge graphs and LLMs to guide data
generation. Our extensive empirical study across 7 clinical NLP tasks and 16
datasets reveals that ClinGen consistently enhances performance across various
tasks, effectively aligning the distribution of real datasets and significantly
enriching the diversity of generated training instances. We will publish our
code and all the generated data in https://github.com/ritaranx/ClinGen.

通过结合临床领域特定知识图谱和大语言模型，在临床自然语言处理中提出了一种创新、资源高效的方法 ClinGen，通过临床知识提取和上下文信息化的大语言模型指导数据生成，持续提升性能，对生成训练实例的多样性和真实数据分布进行有效调整。

知识注入引导：基于大型语言模型的临床文本数据生成评估与推进

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data  Generation with Large Language Models

Clinical NLP tasks such as mental health assessment from text, must take
social constraints into account - the performance maximization must be
constrained by the utmost importance of guaranteeing privacy of user data.
Consumer protection regulations, such as GDPR, generally handle privacy by
restricting data availability, such as requiring to limit user data to 'what is
necessary' for a given purpose. In this work, we reason that providing stricter
formal privacy guarantees, while increasing the volume of user data in the
model, in most cases increases benefit for all parties involved, especially for
the user. We demonstrate our arguments on two existing suicide risk assessment
datasets of Twitter and Reddit posts. We present the first analysis juxtaposing
user history length and differential privacy budgets and elaborate how modeling
additional user context enables utility preservation while maintaining
acceptable user privacy guarantees.

本文探讨在严格保护用户隐私的前提下，提高数据量以增进所有相关方的利益的方法， 并通过对 Twitter 和 Reddit 上两个现有问题的分析，展示了如何在保证用户隐私的同时，通过建模更多的用户上下文，实现效用的保存。