Background Large Language Models (LLMs), enhanced with Clinical Practice
Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS).
However, methods for incorporating CPGs into LLMs are not well studied. Methods
We develop three distinct methods for incorporating CPGs into LLMs: Binary
Decision Tree (BDT), Program-Aided Graph Construction (PAGC), and
Chain-of-Thought-Few-Shot Prompting (CoT-FSP). To evaluate the effectiveness of
the proposed methods, we create a set of synthetic patient descriptions and
conduct both automatic and human evaluation of the responses generated by four
LLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. Zero-Shot Prompting (ZSP) was
used as the baseline method. We focus on CDS for COVID-19 outpatient treatment
as the case study. Results All four LLMs exhibit improved performance when
enhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP
and PAGC in automatic evaluation. All of the proposed methods demonstrated high
performance in human evaluation. Conclusion LLMs enhanced with CPGs demonstrate
superior performance, as compared to plain LLMs with ZSP, in providing accurate
recommendations for COVID-19 outpatient treatment, which also highlights the
potential for broader applications beyond the case study.

通过将临床实践指南（CPGs）融入大型语言模型（LLMs），可以显著提高临床决策支持（CDS）的效果，我们针对 COVID-19 门诊治疗展开的案例研究表明，LLMs 与 CPGs 相结合相对于普通的 LLMs 与零提示（ZSP）提供了更准确的建议，并且还展示了更广泛应用的潜力。

将临床实践指南纳入大型语言模型以提升临床决策支持

Enhancing Large Language Models for Clinical Decision Support by  Incorporating Clinical Practice Guidelines

Ontologies play a crucial role in organizing and representing knowledge.
However, even current ontologies do not encompass all relevant concepts and
relationships. Here, we explore the potential of large language models (LLM) to
expand an existing ontology in a semi-automated fashion. We demonstrate our
approach on the biomedical ontology SNOMED-CT utilizing semantic relation types
from the widely used UMLS semantic network. We propose a method that uses
conversational interactions with an LLM to analyze clinical practice guidelines
(CPGs) and detect the relationships among the new medical concepts that are not
present in SNOMED-CT. Our initial experimentation with the conversational
prompts yielded promising preliminary results given a manually generated gold
standard, directing our future potential improvements.

使用大型语言模型扩展本体的方法在临床实践指南中探索新的医学概念关系，展示了潜在的初步实验结果和未来改进方向。

大型语言模型是否能补充医学本体中缺失的概念和关系？

Can Large Language Models Augment a Biomedical Ontology with missing  Concepts and Relations?

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to
new evidence generated by active research. Currently, CPGs are primarily
published in a document format that is ill-suited for managing this developing
knowledge. A knowledge model of the guidelines document suitable for
programmatic interaction is required. This work proposes an automated method
for extraction of knowledge from National Comprehensive Cancer Network (NCCN)
CPGs in Oncology and generating a structured model containing the retrieved
knowledge. The proposed method was tested using two versions of NCCN Non-Small
Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful
extraction and modeling of knowledge. Three enrichment strategies using Cancer
staging information, Unified Medical Language System (UMLS) Metathesaurus &
National Cancer Institute thesaurus (NCIt) concepts, and Node classification
are also presented to enhance the model towards enabling programmatic traversal
and querying of cancer care guidelines. The Node classification was performed
using a Support Vector Machine (SVM) model, achieving a classification accuracy
of 0.81 with 10-fold cross-validation.

该研究提出了一种从国家综合癌症网络（NCCN）的临床实践指南中提取知识并生成结构化模型的自动化方法，并且采用了三种丰富策略来增强模型以实现程序化遍历和查询癌症护理指南。研究使用支持向量机（SVM）模型进行节点分类，并以 10 折交叉验证的方式获得了 0.81 的分类准确率。

癌症临床实践指南的自动知识建模

Automated Knowledge Modeling for Cancer Clinical Practice Guidelines

Management of chronic diseases such as heart failure (HF) is a major public
health problem. A standard approach to managing chronic diseases by medical
community is to have a committee of experts develop guidelines that all
physicians should follow. Due to their complexity, these guidelines are
difficult to implement and are adopted slowly by the medical community at
large. We have developed a physician advisory system that codes the entire set
of clinical practice guidelines for managing HF using answer set
programming(ASP). In this paper we show how abductive reasoning can be deployed
to find missing symptoms and conditions that the patient must exhibit in order
for a treatment prescribed by a physician to work effectively. Thus, if a
physician does not make an appropriate recommendation or makes a non-adherent
recommendation, our system will advise the physician about symptoms and
conditions that must be in effect for that recommendation to apply. It is under
consideration for acceptance in TPLP.

该研究介绍一种使用答案集编程的医师咨询系统来管理慢性病的做法，通过归纳推理和临床实践指导来判断病人的症状和情况是否适用于医师的治疗建议，以帮助医师尽快采取相应措施。