Cohort studies are of significant importance in the field of healthcare
analysis. However, existing methods typically involve manual, labor-intensive,
and expert-driven pattern definitions or rely on simplistic clustering
techniques that lack medical relevance. Automating cohort studies with
interpretable patterns has great potential to facilitate healthcare analysis
but remains an unmet need in prior research efforts. In this paper, we propose
a cohort auto-discovery model, CohortNet, for interpretable healthcare
analysis, focusing on the effective identification, representation, and
exploitation of cohorts characterized by medically meaningful patterns.
CohortNet initially learns fine-grained patient representations by separately
processing each feature, considering both individual feature trends and feature
interactions at each time step. Subsequently, it classifies each feature into
distinct states and employs a heuristic cohort exploration strategy to
effectively discover substantial cohorts with concrete patterns. For each
identified cohort, it learns comprehensive cohort representations with credible
evidence through associated patient retrieval. Ultimately, given a new patient,
CohortNet can leverage relevant cohorts with distinguished importance, which
can provide a more holistic understanding of the patient's conditions.
Extensive experiments on three real-world datasets demonstrate that it
consistently outperforms state-of-the-art approaches and offers interpretable
insights from diverse perspectives in a top-down fashion.

提出了 CohortNet，一种用于可解释的医疗分析的自动化队研究模型，通过学习细粒度患者表征、发现并利用充满医学意义模式的群体，来为特定患者提供更全面的理解。

CohortNet：用于可解释的医疗保健分析的患者群体发现

CohortNet: Empowering Cohort Discovery for Interpretable Healthcare  Analytics

Electronic Health Records (EHR) are generated from clinical routine care
recording valuable information of broad patient populations, which provide
plentiful opportunities for improving patient management and intervention
strategies in clinical practice. To exploit the enormous potential of EHR data,
a popular EHR data analysis paradigm in machine learning is EHR representation
learning, which first leverages the individual patient's EHR data to learn
informative representations by a backbone, and supports diverse health-care
downstream tasks grounded on the representations. Unfortunately, such a
paradigm fails to access the in-depth analysis of patients' relevance, which is
generally known as cohort studies in clinical practice. Specifically, patients
in the same cohort tend to share similar characteristics, implying their
resemblance in medical conditions such as symptoms or diseases. In this paper,
we propose a universal COhort Representation lEarning (CORE) framework to
augment EHR utilization by leveraging the fine-grained cohort information among
patients. In particular, CORE first develops an explicit patient modeling task
based on the prior knowledge of patients' diagnosis codes, which measures the
latent relevance among patients to adaptively divide the cohorts for each
patient. Based on the constructed cohorts, CORE recodes the pre-extracted EHR
data representation from intra- and inter-cohort perspectives, yielding
augmented EHR data representation learning. CORE is readily applicable to
diverse backbone models, serving as a universal plug-in framework to infuse
cohort information into healthcare methods for boosted performance. We conduct
an extensive experimental evaluation on two real-world datasets, and the
experimental results demonstrate the effectiveness and generalizability of
CORE.

通过提出 COhort Representation lEarning (CORE) 框架，从患者之间的细粒度 cohort 信息出发，对 EHR 数据表示进行增强学习，以提高患者管理和干预策略在临床实践中的应用，为医疗方法注入集体信息以提高性能。