The growing availability of well-organized Electronic Health Records (EHR)
data has enabled the development of various machine learning models towards
disease risk prediction. However, existing risk prediction methods overlook the
heterogeneity of complex diseases, failing to model the potential disease
subtypes regarding their corresponding patient visits and clinical concept
subgroups. In this work, we introduce TACCO, a novel framework that jointly
discovers clusters of clinical concepts and patient visits based on a
hypergraph modeling of EHR data. Specifically, we develop a novel
self-supervised co-clustering framework that can be guided by the risk
prediction task of specific diseases. Furthermore, we enhance the hypergraph
model of EHR data with textual embeddings and enforce the alignment between the
clusters of clinical concepts and patient visits through a contrastive
objective. Comprehensive experiments conducted on the public MIMIC-III dataset
and Emory internal CRADLE dataset over the downstream clinical tasks of
phenotype classification and cardiovascular risk prediction demonstrate an
average 31.25% performance improvement compared to traditional ML baselines and
a 5.26% improvement on top of the vanilla hypergraph model without our
co-clustering mechanism. In-depth model analysis, clustering results analysis,
and clinical case studies further validate the improved utilities and
insightful interpretations delivered by TACCO. Code is available at
this https URL

基于超图建模的 TACCO 方法，通过自监督的共聚类框架，发现临床概念和患者访问的簇类，进一步加强了电子医疗记录数据的超图模型，并通过对比目标实现了临床概念和患者访问的簇类的对齐，实验证明了 TACCO 提供了改进的效用和深入的解释。

TACCO: 基于电子病历数据的疾病亚型划分的临床概念和患者访问的任务引导合并聚类

TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits  for Disease Subtyping based on EHR Data

Medical image classification is a critical problem for healthcare, with the
potential to alleviate the workload of doctors and facilitate diagnoses of
patients. However, two challenges arise when deploying deep learning models to
real-world healthcare applications. First, neural models tend to learn spurious
correlations instead of desired features, which could fall short when
generalizing to new domains (e.g., patients with different ages). Second, these
black-box models lack interpretability. When making diagnostic predictions, it
is important to understand why a model makes a decision for trustworthy and
safety considerations. In this paper, to address these two limitations, we
propose a new paradigm to build robust and interpretable medical image
classifiers with natural language concepts. Specifically, we first query
clinical concepts from GPT-4, then transform latent image features into
explicit concepts with a vision-language model. We systematically evaluate our
method on eight medical image classification datasets to verify its
effectiveness. On challenging datasets with strong confounding factors, our
method can mitigate spurious correlations thus substantially outperform
standard visual encoders and other baselines. Finally, we show how
classification with a small number of concepts brings a level of
interpretability for understanding model decisions through case studies in real
medical data.

通过使用自然语言概念，我们提出了一种建立强大而可解释的医学图像分类器的新范式，有效地解决了深度学习模型在医疗行业应用中学习虚假相关性而不是期望特征以及缺乏可解释性的问题。

通过概念瓶颈模型实现强大且可解释的医学图像分类器

Robust and Interpretable Medical Image Classifiers via Concept  Bottleneck Models

Automated decision support can accelerate tedious tasks as users can focus
their attention where it is needed most. However, a key concern is whether
users overly trust or cede agency to automation. In this paper, we investigate
the effects of introducing automation to annotating clinical texts--a
multi-step, error-prone task of identifying clinical concepts (e.g.,
procedures) in medical notes, and mapping them to labels in a large ontology.
We consider two forms of decision aid: recommending which labels to map
concepts to, and pre-populating annotation suggestions. Through laboratory
studies, we find that 18 clinicians generally build intuition of when to rely
on automation and when to exercise their own judgement. However, when presented
with fully pre-populated suggestions, these expert users exhibit less agency:
accepting improper mentions, and taking less initiative in creating additional
annotations. Our findings inform how systems and algorithms should be designed
to mitigate the observed issues.

本文研究在临床记录上添加自动标记的影响，发现专家用户对于何时依靠自动处理和何时依靠自己的判断有建立信心的直觉，但当提供全自动处理时，这些用户表现出比较低的自主性和更容易接受错误的提议。