In this study, we aim to address the task of assertion detection when
extracting medical concepts from clinical notes, a key process in clinical
natural language processing (NLP). Assertion detection in clinical NLP usually
involves identifying assertion types for medical concepts in the clinical text,
namely certainty (whether the medical concept is positive, negated, possible,
or hypothetical), temporality (whether the medical concept is for present or
the past history), and experiencer (whether the medical concept is described
for the patient or a family member). These assertion types are essential for
healthcare professionals to quickly and clearly understand the context of
medical conditions from unstructured clinical texts, directly influencing the
quality and outcomes of patient care. Although widely used, traditional
methods, particularly rule-based NLP systems and machine learning or deep
learning models, demand intensive manual efforts to create patterns and tend to
overlook less common assertion types, leading to an incomplete understanding of
the context. To address this challenge, our research introduces a novel
methodology that utilizes Large Language Models (LLMs) pre-trained on a vast
array of medical data for assertion detection. We enhanced the current method
with advanced reasoning techniques, including Tree of Thought (ToT), Chain of
Thought (CoT), and Self-Consistency (SC), and refine it further with Low-Rank
Adaptation (LoRA) fine-tuning. We first evaluated the model on the i2b2 2010
assertion dataset. Our method achieved a micro-averaged F-1 of 0.89, with 0.11
improvements over the previous works. To further assess the generalizability of
our approach, we extended our evaluation to a local dataset that focused on
sleep concept extraction. Our approach achieved an F-1 of 0.74, which is 0.31
higher than the previous method.

通过使用基于大量医学数据预训练的大型语言模型（LLMs）和先进的推理技术，我们介绍了一种用于检测医学概念的断言的新方法，该方法在传统方法的基础上进一步优化，并在无结构的临床文本中取得了良好的性能。

断言检测大型语言模型上下文学习 LoRA 微调

Assertion Detection Large Language Model In-context Learning LoRA  Fine-tuning

Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that
asks us to plan collision-free paths for a team of cooperative agents. In this
work, we show that one of the reasons why MAPF is so hard to solve is due to a
phenomenon called pairwise symmetry, which occurs when two agents have many
different paths to their target locations, all of which appear promising, but
every combination of them results in a collision. We identify several classes
of pairwise symmetries and show that each one arises commonly in practice and
can produce an exponential explosion in the space of possible collision
resolutions, leading to unacceptable runtimes for current state-of-the-art
(bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning
techniques that detect the symmetries efficiently as they arise and resolve
them by using specialized constraints to eliminate all permutations of pairwise
colliding paths in a single branching step. We implement these ideas in the
context of the leading optimal MAPF algorithm CBS and show that the addition of
the symmetry reasoning techniques can have a dramatic positive effect on its
performance - we report a reduction in the number of node expansions by up to
four orders of magnitude and an increase in scalability by up to thirty times.
These gains allow us to solve to optimality a variety of challenging MAPF
instances previously considered out of reach for CBS.

本文针对多智能体路径规划中的对称性问题，提出了一种通过推理技术解决对称性问题的方法，并将其应用于 CBS 算法，实现了在性能和效率方面的显著提升。