Interesting Subgroups Research Articles

EsmamDS: A more diverse exceptional survival model mining approach

In this work we present an Ant Colony Optimization heuristic to find subgroups with exceptional behavior in time-to-event data. The area of time-to-event or survival data analysis has its basis in statistics, where the main goal is to predict if and when an event will happen. In other words, the main goal in survival analysis has long been to build global models able to predict the time for the occurrence of an event. Nevertheless, very often predictive models are used to compare stratified data in order to evaluate whether a variable is associated or not with the outcome. For instance, patients might be stratified according to a treatment variable (placebo or not) to compare models (survival curves) and decide on the effectiveness of the treatment. Although this is an effective approach if the variable of interest is already known, it does not provide an alternative for the cases where specialists do not know how to stratify the data, that is, if they do not know which variable could be related to the outcome. Our approach targets exactly this. Our method seeks combinations of variables that are associated, i.e. describe, subgroups of individuals with unexpected or exceptional survival curves. In this sense, we complement the literature with a descriptive approach that is able to find and characterize those groups for specialists. Our method is based on the framework of exceptional model mining. It improves on a preliminary version presented in a conference. The main enhancement was to redesign our heuristic to retrieve interesting and diverse subgroups while minimizing three aspects of redundancy: coverage; description; and model. Our second extension regards how the quality function is applied. We now allow users to control whether the quality measure compares subgroups against the population, or against individuals that do not satisfy the descriptive rule. Third, we conduct further experiments to compare the performance of our approach to state of the art algorithms with real world benchmark data sets. Finally, we also present a case study showing a possible application of our method in the bioinformatics/health domain.

EARLY RESULTS OF THE PROSPECTIVE MULTIDISCIPLINARY HAMA COHORT: DESCRIPTION OF THE FIRST 302 PATIENTS, COMPARISON WITH ALREADY PUBLISHED COHORT AND NOVEL INSIGHTS

Objective: Malignant hypertension has not disappeared and remains the most severe form of hypertension. More than 100 years after its description, many points remain unanswered. Mechanisms, definition and optimal treatment are still discussed. In 2019 we decided to launch a prospective multicenter multidisciplinary cohort in France try filling these gaps. Thjs study aims to describe the baseline characteristics of the first 302 included patients and compare these data to already published cohorts. Design and method: We included patient with severe hypertension and severe hypertensive retinopathy and patients filling the HYP MOD definition from a broad range of department (cardiology, nephrology, neurology intensive care unit, emergency department, internal medicine). We collected clinical data at admission, biological, imaging and target organ damage along with social and demographic data. We also recorded diagnostic and therapeutic management, adverse events during hospitalization and characteristics at discharge. Results: We recruited 302 patients in 32 month (105 / year) among 40 centres and different specialities. They are mostly young men (68%, mean age 48.7 +/- 14.5 years), with several interesting subgroups. Around one third of our patients were less than 40 years old, one third were from non-European origin, 13.6% were included through the multiorgan damage definition, without fundus severe injuries. Target organ damage involves the eye in 86% of patients, kidney in 54%, heart in 56%, brain in 33% and Thrombotic Microangiopathy in 13%. Our patients with severe retinopathy are very similar with those included in the most important cohorts already published. Interestingly, 22.8% of patients were treated without use of IV therapy, most patients were untreated or showed poor observance of treatments and a significant proportion had normal or low renin level. Conclusions: These preliminary findings already challenge long standing dogma, raise many questions and provide a strong basis to address them in ancillary studies of the cohort.

RegCFinder: targeted discovery of genomic subregions with differential read density.

To date, no methods are available for the targeted identification of genomic subregions with differences in sequencing read distributions between two conditions. Existing approaches either only determine absolute read number changes, require predefined subdivisions of input windows or average across multiple genes. Here, we present RegCFinder, which automatically identifies subregions of input windows with differences in read density between two conditions. For this purpose, the problem is defined as an instance of the all maximum scoring subsequences problem, which can be solved in linear time. Subsequently, statistical significance and differential usage of identified subregions are determined with DEXSeq. RegCFinder allows flexible definition of input windows to target the analysis to any regions of interests, e.g. promoters, gene bodies, peak regions and more. Furthermore, any type of sequencing assay can be used as input; thus, RegCFinder lends itself to a wide range of applications. We illustrate the usefulness of RegCFinder on two applications, where we can both confirm previous results and identify interesting gene subgroups with distinctive changes in read distributions. RegCFinder is implemented as a workflow for the workflow management system Watchdog and available at: https://github.com/watchdog-wms/watchdog-wms-workflows/. Supplementary data are available at Bioinformatics Advances online.

Microscopy-based phenotypic profiling of infection by Staphylococcus aureus clinical isolates reveals intracellular lifestyle as a prevalent feature

Staphylococcus aureus is increasingly recognized as a facultative intracellular pathogen, although the significance and pervasiveness of its intracellular lifestyle remain controversial. Here, we applied fluorescence microscopy-based infection assays and automated image analysis to profile the interaction of 191 S. aureus isolates from patients with bone/joint infections, bacteremia, and infective endocarditis, with four host cell types, at five times post-infection. This multiparametric analysis revealed that almost all isolates are internalized and that a large fraction replicate and persist within host cells, presenting distinct infection profiles in non-professional vs. professional phagocytes. Phenotypic clustering highlighted interesting sub-groups, including one comprising isolates exhibiting high intracellular replication and inducing delayed host death in vitro and in vivo. These isolates are deficient for the cysteine protease staphopain A. This study establishes S. aureus intracellular lifestyle as a prevalent feature of infection, with potential implications for the effective treatment of staphylococcal infections.

Subgroup discovery in structural equation models.

Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In the present article, we present a new approach combining subgroup discovery-a well-established toolkit of supervised learning algorithms and techniques from the field of computer science-with structural equation models termed SubgroupSEM. We provide an overview and comparison of three approaches to modeling and detecting heterogeneous groups in structural equation models, namely, finite mixture models, SEM trees, and SubgroupSEM. We provide a step-by-step guide to applying subgroup discovery techniques for structural equation models, followed by a detailed and illustrated presentation of pruning strategies and four subgroup discovery algorithms. Finally, the SubgroupSEM approach will be illustrated on two real data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by examples of the R package subgroupsem, which is a viable implementation of our approach for applied researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The physical nature of the ultrashort spike-ring interaction in iron maiden molecules.

The so-called 'iron maiden' molecules belong to one of the most interesting subgroups of cyclophanes due to the presence of the ultrashort interaction between the CX apical bond and the benzene ring. This article presents an in-depth theoretical study of 16 'iron maiden' molecules, in which X=H, F, Cl or Br and the side chains are of various lengths and types: CSC, CSCC, CCC, and CCCC. It is shown that the H → F → Cl → Br substitution leads to a significant expansion of the 'iron maiden' molecule. Shorter chains lead to more pronounced effects, while insertion of sulfur atoms into the side chains lowers them. Structural changes are associated with an increase in energetic destabilization of X. Moreover, unlike for H, in the case of X=halogen, the out → in isomerization is energetically disadvantageous. The 'iron maiden' molecules are characterized by the presence of only three X⋯CAr bond paths. Particularly noteworthy are unusually large (even up to 32) values of the X⋯CAr bond ellipticity, which results from flat electron density distribution. The X⋯π interaction in each of the investigated 'iron maiden' molecule turned out to be multi-center, stabilizing and almost purely covalent in nature as indicated by the definitely dominant percentage (94.8%-101.6%) of the exchange-correlation energy. The spatial hindrance within the 'iron maiden' molecules appears to be not so much due to the X⋯π repulsion, but due to unfavorable steric interactions between X and the CC side bonds. It is also confirmed that some CH⋯HC interactions in aliphatic chains can be very weakly stabilizing.

A Weakly Supervised Clustering Method for Cancer Subgroup Identification

Identifying subgroups of cancer patients is important as it opens up possibilities for targeted therapeutics. A widely applied approach is to group patients with unsupervised clustering techniques based on molecular data of tumor samples. The patient clusters are found to be of interest if they can be associated with a clinical outcome variable such as the survival of patients. However, these clinical variables of interest do not participate in the clustering decisions. We propose an approach, WSURFC (Weakly Supervised Random Forest Clustering), where the clustering process is weakly supervised with a clinical variable of interest. The supervision step is handled by learning a similarity metric with features that are selected to predict this clinical variable. More specifically, WSURFC involves a random forest classifier-training step to predict the clinical variable, in this case, the survival class. Subsequently, the internal nodes are used to derive a random forest similarity metric among the pairs of samples. In this way, the clustering step utilizes the nonlinear subspace of the original features learned in the classification step. We first demonstrate WSURFC on hand-written digit datasets, where WSURFC is able to capture salient structural similarities of digit pairs. Next, we apply WSURFC to find breast cancer subtypes using mRNA, protein, and microRNA expressions as features. Our results on breast cancer show that WSURFC could identify interesting patient subgroups more effectively than the widely adopted methods.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients’ records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients’ disease evolution by refined data clustering in three points in time over ten years, and generating patients’ migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work.The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Excessive Vitamin A Supplementation Increased the Incidence of Acute Respiratory Tract Infections: A Systematic Review and Meta-Analysis.

(1) Background: vitamin A deficiency (VAD) is highly prevalent in children living in poor conditions. It has been suggested that vitamin A supplementation (VAS) may reduce the risk of acute respiratory tract infections (ARTI). Our study provides updates on the effects of oral VAS (alone) in children on ARTI and further explores the effect on interesting subgroups. (2) Methods: eight databases were systematically searched from their inception until 5 July 2021. The assessments of inclusion criteria, extraction of data, and data synthesis were carried out independently by two reviewers. (3) Results: a total of 26 randomized trials involving 50,944 participants fulfilled the inclusion criteria. There was no significant association of VAS with the incidence of ARTI compared with the placebo (RR 1.03, 95% CI 0.92 to 1.15). Subgroup analyses showed that VAS higher than WHO recommendations increased the incidence of ARTI by 13% (RR 1.13, 95% CI 1.07 to 1.20), and in the high-dose intervention group, the incidence rate among well-nourished children rose by 66% (RR 1.66, 95% CI 1.30 to 2.11). (4) Conclusions: no more beneficial effects were seen with VAS in children in the prevention or recovery of acute respiratory infections. Excessive VAS may increase the incidence of ARTI in children with normal nutritional status.

Uni- and multivariate probability density models for numeric subgroup discovery

Subgroup Discovery is a supervised, exploratory data mining paradigm that aims to identify subsets of a dataset that show interesting behaviour with respect to some designated target attribute. The way in which such distributional differences are quantified varies with the target attribute type. This work concerns continuous targets, which are important in many practical applications. For such targets, differences are often quantified using z-score and similar measures that compare simple statistics such as the mean and variance of the subset and the data. However, most distributions are not fully determined by their mean and variance alone. As a result, measures of distributional difference solely based on such simple statistics will miss potentially interesting subgroups. This work proposes methods to recognise distributional differences in a much broader sense. To this end, density estimation is performed using histogram and kernel density estimation techniques. In the spirit of Exceptional Model Mining, the proposed methods are extended to deal with multiple continuous target attributes, such that comparisons are not restricted to univariate distributions, but are available for joint distributions of any dimensionality. The methods can be incorporated easily into existing Subgroup Discovery frameworks, so no new frameworks are developed.

Anytime mining of sequential discriminative patterns in labeled sequences

It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of patterns covering sets of objects having interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for both transactional and numerical data, discovering subgroups within labeled sequential data has been much less studied. First, we propose an anytime algorithm SeqScout that discovers interesting subgroups w.r.t. a chosen quality measure. This is a sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. For a given budget, it finds a collection of local optima in the search space of descriptions and thus, subgroups. It requires a light configuration and is independent from the quality measure used for pattern scoring. We also introduce a second anytime algorithm MCTSExtent that pushes further the idea of a better trade-off between exploration and exploitation of a sampling strategy over the search space. To the best of our knowledge, this is the first time that the Monte Carlo Tree Search framework is exploited in a sequential data mining setting. We have conducted a thorough and comprehensive evaluation of our algorithms on several datasets to illustrate their added value, and we discuss their qualitative and quantitative results.

On the universal ellipsitomic KZB connection

We construct a twisted version of the genus one universal Knizhnik-Zamolodchikov-Bernard (KZB) connection introduced by Calaque-Enriquez-Etingof, that we call the ellipsitomic KZB connection. This is a flat connection on a principal bundle over the moduli space of $\Gamma$-structured elliptic curves with marked points, where $\Gamma=\mathbb{Z}/M\mathbb{Z}\times\mathbb{Z}/N\mathbb{Z}$, and $M,N\geq1$ are two integers. It restricts to a flat connection on $\Gamma$-twisted configuration spaces of points on elliptic curves, which can be used to construct a filtered-formality isomorphism for some interesting subgroups of the pure braid group on the torus. We show that the universal ellipsitomic KZB connection realizes as the usual KZB connection associated with elliptic dynamical $r$-matrices with spectral parameter, and finally, also produces representations of cyclotomic Cherednik algebras.

MiSoSouP

We present MiSoSouP, a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different popular interestingness measures, from a random sample of a transactional dataset. We describe a new formulation of these measures as functions of averages, that makes it possible to approximate them using sampling. We then discuss how pseudodimension, a key concept from statistical learning theory, relates to the sample size needed to obtain an high-quality approximation of the most interesting subgroups. We prove an upper bound on the pseudodimension of the problem at hand, which depends on characteristic quantities of the dataset and of the language of patterns of interest. This upper bound then leads to small sample sizes. Our evaluation on real datasets shows that MiSoSouP outperforms state-of-the-art algorithms offering the same guarantees, and it vastly speeds up the discovery of subgroups w.r.t. analyzing the whole dataset.

Who decides? On those involved in the choice of school for educational careers of children at the end of primary school

The choice of school for the secondary school sets the course for the future academic and professional career. This study clarifies who is the main decision maker in the choice of school based on an empirical survey using a questionnaire in which 828 parents from southern Lower Austria participated. In accordance with the literature, 618 parents state that they made the educational choice together with their children, with interesting significant subgroups regarding the gender of the child and the parents, the education of the parents, the siblings’ success and the parents’ educational aspirations.

CPS analysis: self-contained validation of biomedical data clustering

Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. We have developed a toolkit called covering point set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. The method is implemented in an R package called OTclust, available on CRAN. lzz46@psu.edu or jiali@psu.edu. Supplementary data are available at Bioinformatics online.

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs. We utilized Latent Dirichlet Allocation (LDA), a generative probabilistic model, and proposed a novel model named Poisson Dirichlet Model (PDM), which extends the LDA approach using a Poisson distribution to model patients' disease diagnoses and to alleviate age and sex factors by considering both observed and expected observations. In the empirical experiments, we evaluated LDA and PDM on three patient cohorts, namely Osteoporosis, Delirium/Dementia, and Chronic Obstructive Pulmonary Disease (COPD)/Bronchiectasis Cohorts, with their EHR data retrieved from the Rochester Epidemiology Project (REP) medical records linkage system, for the discovery of latent disease clusters and patient subgroups. We compared the effectiveness of LDA and PDM in identifying disease clusters through the visualization of disease representations. We tested the performance of LDA and PDM in differentiating patient subgroups through survival analysis, as well as statistical analysis of demographics and Elixhauser Comorbidity Index (ECI) scores in those subgroups. The experimental results show that the proposed PDM could effectively identify distinguished disease clusters based on the latent patterns hidden in the EHR data by alleviating the impact of age and sex, and that LDA could stratify patients into differentiable subgroups with larger p-values than PDM. However, those subgroups identified by LDA are highly associated with patients' age and sex. The subgroups discovered by PDM might imply the underlying patterns of diseases of greater interest in epidemiology research due to the alleviation of age and sex. Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.

Visualizing event sequence game data to understand player\u2019s skill growth through behavior complexity

Analysis of game data is used to study player behavior. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. In this paper, we present a visualization system to help learning expert to understand how actions, timing and the resulting strategy change with regard to the solution complexity. To establish a novel perspective into the patterns not only in action choices but also in behavior complexity, we designed an interactive, customized line chart to track how complexity and performance change at each stage of skill acquisition. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. Evaluation with expert users shows that the system effectively reduced their time and effort in finding interesting subgroups and gave them unexplored angles of behavior complexity to contemplate player’s skill growth. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games.Graphical abstract

Rank correlated subgroup discovery

Subgroup discovery (SD) and exceptional model mining (EMM), its generalization to handle more complex targets, are two mature fields at the frontier of data mining and machine learning. More precisely, EMM aims to find coherent subgroups of a dataset where multiple targets interact in an unusual way. Correlation model classes have already been defined to discover interesting subgroups when dealing with two numerical targets. However, in this supervised setting, the two numerical targets are fixed before the subgroup search. To make unsupervised exploration possible, we propose to search for arbitrary subsets of numerical targets whose correlation is exceptional for an automatically found subgroup. This involves solving two challenges: the definition of a model that evaluates the interest of a subgroup for a subset of numerical targets and the definition of a pattern language that enumerates both subgroups and targets and lends itself to effective research strategies. We propose an integrated solution to both challenges. We introduce the problem of rank-correlated subgroup discovery with an arbitrary subset of numerical targets. A rank-correlated subgroup is identified by both conditions on descriptive attributes, whether numeric or nominal, and a pattern on numeric attributes that captures (positive or negative) rank correlations based on a generalization of the Kendall’s τ. We define a new branch-and-bound algorithm that exploits some pruning properties based on two upper-bounds and a closure property. An empirical study on several datasets demonstrates the efficiency and the effectiveness of the algorithm.

On Jones’ subgroup of R. Thompson’s group T

Jones introduced unitary representations for the Thompson groups [Formula: see text] and [Formula: see text] from a given subfactor planar algebra. Some interesting subgroups arise as the stabilizer of certain vector, in particular the Jones subgroups [Formula: see text] and [Formula: see text]. Golan and Sapir studied [Formula: see text] and identified it as a copy of the Thompson group [Formula: see text]. In this paper, we completely describe [Formula: see text] and show that [Formula: see text] coincides with its commensurator in [Formula: see text], implying that the corresponding unitary representation is irreducible. We also generalize the notion of the Stallings 2-core for diagram groups to [Formula: see text], showing that [Formula: see text] and [Formula: see text] are not isomorphic, but as annular diagram groups they have very similar presentations.

Identifying key factors of student academic performance by subgroup discovery

Identifying the factors that influence student academic performance is essential to provide timely and effective support interventions. The data collected during enrolment and after commencement into a course provide an important source of information to assist with identifying potential risk indicators associated with poor academic performance and attrition. Both predictive and descriptive data mining techniques have been applied on educational data to discover the significant reasons behind student performance. These techniques have their own advantages and limitations. For example, predictive techniques tend to maximise accuracy for correctly classifying students, while the descriptive techniques simply search for interesting student features without considering their academic outcome. Subgroup discovery is a data mining method which takes the advantages of both predictive and descriptive approaches. This study uses subgroup discovery to extract significant factors of student performance for a certain outcome (Pass or Fail). In this work, we have utilised student demographic and academic data recorded at enrolment, as well as course assessment and participation data retrieved from the institution’s learning management system (Moodle) to detect the factors affecting student performance. The results have demonstrated the effectiveness of the subgroup discovery method in general in identifying the factors, and the pros and cons of some popular subgroup discovery algorithms used in this research. From the experiments, it has been found that students, who have indigent socio-economic background or been admitted based on special entry requirement, are most likely to fail. The experiments on Moodle data have revealed that students having lower level of access to the course resources and forum have higher possibility of being unsuccessful. From the combined data, we have identified some interesting subgroups which are not detected using enrolment or Moodle data separately. It has been found that those students, who study off-campus or part-time and have a low level of contributions to the course learning activities, are more likely to be the low-performing students.