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

Why does a phenomenon occur? Addressing this question is central to most
scientific inquiries based on empirical observations, and often heavily relies
on simulations of scientific models. As models become more intricate,
deciphering the causes behind these phenomena in high-dimensional spaces of
interconnected variables becomes increasingly challenging. Causal machine
learning may assist scientists in the discovery of relevant and interpretable
patterns of causation in simulations. We introduce Targeted Causal Reduction
(TCR), a method for turning complex models into a concise set of causal factors
that explain a specific target phenomenon. We derive an information theoretic
objective to learn TCR from interventional data or simulations and propose
algorithms to optimize this objective efficiently. TCR's ability to generate
interpretable high-level explanations from complex models is demonstrated on
toy and mechanical systems, illustrating its potential to assist scientists in
the study of complex phenomena in a broad range of disciplines.

为了解决复杂模型中高维空间的因果关系模式识别的问题，我们引入了目标因果约简（Targeted Causal Reduction，TCR）方法来将复杂模型转化为一组简洁的因果因素，并通过玩具和机械系统的实例验证了 TCR 从复杂模型中生成可解释的高层解释的能力，展示了它在广泛学科中辅助科学家研究复杂现象的潜力。

有针对性地减少因果模型

Targeted Reduction of Causal Models

Discovering interpretable patterns for classification of sequential data is
of key importance for a variety of fields, ranging from genomics to fraud
detection or more generally interpretable decision-making. In this paper, we
propose a novel differentiable fully interpretable method to discover both
local and global patterns (i.e. catching a relative or absolute temporal
dependency) for rule-based binary classification. It consists of a
convolutional binary neural network with an interpretable neural filter and a
training strategy based on dynamically-enforced sparsity. We demonstrate the
validity and usefulness of the approach on synthetic datasets and on an
open-source peptides dataset. Key to this end-to-end differentiable method is
that the expressive patterns used in the rules are learned alongside the rules
themselves.

本文提出了一种新颖的可微分全可解释性方法，用于发现规则为基础的二元分类的本地和全局模式，并在开源肽数据集上展示了方法的有效性和实用性。

基于神经网络的序列数据分类规则学习

Neural-based classification rule learning for sequential data

Anomaly detection has numerous applications and has been studied vastly. We
consider a complementary problem that has a much sparser literature: anomaly
description. Interpretation of anomalies is crucial for practitioners for
sense-making, troubleshooting, and planning actions. To this end, we present a
new approach called x-PACS (for eXplaining Patterns of Anomalies with
Characterizing Subspaces), which "reverse-engineers" the known anomalies by
identifying (1) the groups (or patterns) that they form, and (2) the
characterizing subspace and feature rules that separate each anomalous pattern
from normal instances. Explaining anomalies in groups not only saves analyst
time and gives insight into various types of anomalies, but also draws
attention to potentially critical, repeating anomalies.
In developing x-PACS, we first construct a desiderata for the anomaly
description problem. From a descriptive data mining perspective, our method
exhibits five desired properties in our desiderata. Namely, it can unearth
anomalous patterns (i) of multiple different types, (ii) hidden in arbitrary
subspaces of a high dimensional space, (iii) interpretable by the analysts,
(iv) different from normal patterns of the data, and finally (v) succinct,
providing the shortest data description. Furthermore, x-PACS is highly
parallelizable and scales linearly in terms of data size.
No existing work on anomaly description satisfies all of these properties
simultaneously. While not our primary goal, the anomalous patterns we find
serve as interpretable "signatures" and can be used for detection. We show the
effectiveness of x-PACS in explanation as well as detection on real-world
datasets as compared to state-of-the-art.

本论文提出了一种新的方法 x-PACS，用于解释异常模式，从而帮助分析师省略了分析各种异常模式的繁琐过程，同时可发现关键的、重复出现的异常模式，并通过实验证明该方法比现有方法更具解释性和有效性。