Objective: Identifying study-eligible patients within clinical databases is a
critical step in clinical research. However, accurate query design typically
requires extensive technical and biomedical expertise. We sought to create a
system capable of generating data model-agnostic queries while also providing
novel logical reasoning capabilities for complex clinical trial eligibility
criteria.
Materials and Methods: The task of query creation from eligibility criteria
requires solving several text-processing problems, including named entity
recognition and relation extraction, sequence-to-sequence transformation,
normalization, and reasoning. We incorporated hybrid deep learning and
rule-based modules for these, as well as a knowledge base of the Unified
Medical Language System (UMLS) and linked ontologies. To enable data-model
agnostic query creation, we introduce a novel method for tagging database
schema elements using UMLS concepts. To evaluate our system, called LeafAI, we
compared the capability of LeafAI to a human database programmer to identify
patients who had been enrolled in 8 clinical trials conducted at our
institution. We measured performance by the number of actual enrolled patients
matched by generated queries.
Results: LeafAI matched a mean 43% of enrolled patients with 27,225 eligible
across 8 clinical trials, compared to 27% matched and 14,587 eligible in
queries by a human database programmer. The human programmer spent 26 total
hours crafting queries compared to several minutes by LeafAI.
Conclusions: Our work contributes a state-of-the-art data model-agnostic
query generation system capable of conditional reasoning using a knowledge
base. We demonstrate that LeafAI can rival a human programmer in finding
patients eligible for clinical trials.

利用深度学习和基于规则的模块生成具有新颖逻辑推理能力的、数据模型非相关性和基于 UMLS（Unified Medical Language System）的数据库查询方法，以帮助临床研究中的受试者筛选。使用 LeafAI 系统的查询结果表明，与人类数据库程序员相比，其找到的适宜受试者占比更高。

LeafAI: 临床队列发现的查询生成器，可与人类程序员媲美

LeafAI: query generator for clinical cohort discovery rivaling a human  programmer

The process of manually searching for relevant instances in, and extracting
information from, clinical databases underpin a multitude of clinical tasks.
Such tasks include disease diagnosis, clinical trial recruitment, and
continuing medical education. This manual search-and-extract process, however,
has been hampered by the growth of large-scale clinical databases and the
increased prevalence of unlabelled instances. To address this challenge, we
propose a supervised contrastive learning framework, CROCS, where
representations of cardiac signals associated with a set of patient-specific
attributes (e.g., disease class, sex, age) are attracted to learnable
embeddings entitled clinical prototypes. We exploit such prototypes for both
the clustering and retrieval of unlabelled cardiac signals based on multiple
patient attributes. We show that CROCS outperforms the state-of-the-art method,
DTC, when clustering and also retrieves relevant cardiac signals from a large
database. We also show that clinical prototypes adopt a semantically meaningful
arrangement based on patient attributes and thus confer a high degree of
interpretability.

该研究提出一种监督式对比学习框架 CROCS，它可以将与病人特定属性（如疾 病类别、性别、年龄）相关联的心脏信号的表示学习到可以学习的临床原型嵌入 中。通过使用这些原型，可以根据多个病人属性聚类和检索无标签的心脏信号。该 框架在聚类和从大型数据库检索相关心脏信号方面优于现有的方法，并且临床原型 根据病人属性采用语义相关性布局，从而提供了高度可解释性。