Identifying patient cohorts is fundamental to numerous healthcare tasks,
including clinical trial recruitment and retrospective studies. Current cohort
retrieval methods in healthcare organizations rely on automated queries of
structured data combined with manual curation, which are time-consuming,
labor-intensive, and often yield low-quality results. Recent advancements in
large language models (LLMs) and information retrieval (IR) offer promising
avenues to revolutionize these systems. Major challenges include managing
extensive eligibility criteria and handling the longitudinal nature of
unstructured Electronic Medical Records (EMRs) while ensuring that the solution
remains cost-effective for real-world application. This paper introduces a new
task, Automatic Cohort Retrieval (ACR), and evaluates the performance of LLMs
and commercial, domain-specific neuro-symbolic approaches. We provide a
benchmark task, a query dataset, an EMR dataset, and an evaluation framework.
Our findings underscore the necessity for efficient, high-quality ACR systems
capable of longitudinal reasoning across extensive patient databases.

自动检索病例组是许多医疗任务的基础，本文引入了一种新的任务 —— 自动检索病例组，并评估了大型语言模型和领域特定的神经符号方法的性能。

ACR：自动队友检索基准

ACR: A Benchmark for Automatic Cohort Retrieval

Deep Learning (DL) can predict biomarkers directly from digitized cancer
histology in a weakly-supervised setting. Recently, the prediction of
continuous biomarkers through regression-based DL has seen an increasing
interest. Nonetheless, clinical decision making often requires a categorical
outcome. Consequently, we developed a weakly-supervised joint multi-task
Transformer architecture which has been trained and evaluated on four public
patient cohorts for the prediction of two key predictive biomarkers,
microsatellite instability (MSI) and homologous recombination deficiency (HRD),
trained with auxiliary regression tasks related to the tumor microenvironment.
Moreover, we perform a comprehensive benchmark of 16 approaches of task
balancing for weakly-supervised joint multi-task learning in computational
pathology. Using our novel approach, we improve over the state-of-the-art area
under the receiver operating characteristic by +7.7% and +4.1%, as well as
yielding better clustering of latent embeddings by +8% and +5% for the
prediction of MSI and HRD in external cohorts, respectively.

通过弱监督的方式，我们开发了一个联合多任务 Transformer 架构，用于预测肿瘤学领域的两个关键预测生物标志物：微卫星不稳定性和同源重组缺陷，并通过辅助回归任务相关于肿瘤微环境，对此进行了培训和评估。使用我们的新方法，在外部队列中，相对于接收器操作特性下的最佳结果，对于预测微卫星不稳定性和同源重组缺陷，分别提高了 7.7％和 4.1％，同时通过 8％和 5％的更好嵌入聚类获得了更好的结果。

联合多任务学习改进计算机病理学中的弱监督生物标记物预测

Joint multi-task learning improves weakly-supervised biomarker  prediction in computational pathology

When re-structuring patient cohorts into so-called population graphs,
initially independent data points can be incorporated into one interconnected
graph structure. This population graph can then be used for medical downstream
tasks using graph neural networks (GNNs). The construction of a suitable graph
structure is a challenging step in the learning pipeline that can have severe
impact on model performance. To this end, different graph assessment metrics
have been introduced to evaluate graph structures. However, these metrics are
limited to classification tasks and discrete adjacency matrices, only covering
a small subset of real-world applications. In this work, we introduce extended
graph assessment metrics (GAMs) for regression tasks and continuous adjacency
matrices. We focus on two GAMs in specific: \textit{homophily} and
\textit{cross-class neighbourhood similarity} (CCNS). We extend the notion of
GAMs to more than one hop, define homophily for regression tasks, as well as
continuous adjacency matrices, and propose a light-weight CCNS distance for
discrete and continuous adjacency matrices. We show the correlation of these
metrics with model performance on different medical population graphs and under
different learning settings.

通过引入扩展图评估指标（GAMs），我们研究了患者队列的重组、人口图形的构建以及使用图神经网络进行医学相关的任务，展示了这些评估指标与模型性能之间的相关性。