arXiv subjects

Sohan Seth

Publications and source records attributed to Sohan Seth.

18 recordsLinked to original sources

arXiv2026

Bayesian Supervised Causal Clustering

Finding patient subgroups with similar characteristics is crucial for personalized decision-making in various disciplines such as healthcare and policy evaluation. While most existing approaches rely on unsupervised clustering methods, there is a growing trend toward using supervised clustering methods that identify operationalizable subgroups in the context of a specific outcome of interest. We propose Bayesian Supervised Causal Clustering (BSCC), with treatment effect as outcome to guide the clustering process. BSCC identifies homogenous subgroups of individuals who are similar in their covariate profiles as well as their treatment effects. We evaluate BSCC on simulated datasets as well as real-world dataset from the third International Stroke Trial to assess the practical usefulness of the framework.

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arXiv2026

Modeling mental health trajectories during the COVID-19 pandemic using UK-wide data in the presence of sociodemographic variables

Background: The negative effects of the COVID-19 pandemic on the mental health and well-being of populations are an important public health issue. Our study aims to determine the underlying factors shaping mental health trajectories during the COVID-19 pandemic in the UK. Methods: Data from the Understanding Society COVID-19 Study were utilized and the core analysis focussed on GHQ36 scores as the outcome variable. We used GAMs to evaluate trends over time and the role of sociodemographic variables, i.e., age, sex, ethnicity, country of residence (in UK), job status (employment), household income, living with a partner, living with children under age 16, and living with a long-term illness, on the variation of mental health during the study period. Results: Statistically significant differences in mental health were observed for age, sex,ethnicity, country of residence (in UK), job status (employment), household income, living with a partner, living with children under age 16, and living with a long-term illness. Women experienced higher GHQ36 scores relative to men with the GHQ36 score expected to increase by 1.260 (95%CI: 1.176, 1.345). Individuals living without a partner were expected to have higher GHQ36 scores, of 1.050 (95%CI: 0.949, 1.148) more than those living with a partner, and age groups 16-34, 35-44, 45-54, 55-64 experienced higher GHQ36 scores relative to those who were 65+. Individuals with relatively lower household income were likely to have poorer mental health relative to those who were more well off. Conclusion: This study identifies key demographic determinants shaping mental health trajectories during the COVID-19 pandemic in the UK. Policies aiming to reduce mental health inequalities should target women, youth, individuals living without a partner, individuals living with children under 16, individuals with a long-term illness, and lower income families.

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arXiv2025

TraCeR: Transformer-Based Competing Risk Analysis with Longitudinal Covariates

Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating longitudinal covariates, with prior work largely focusing on cross-sectional features, and in assessing calibration of these models, with research primarily focusing on discrimination during evaluation. We introduce TraCeR, a transformer-based survival analysis framework for incorporating longitudinal covariates. Based on a factorized self-attention architecture, TraCeR estimates the hazard function from a sequence of measurements, naturally capturing temporal covariate interactions without assumptions about the underlying data-generating process. The framework is inherently designed to handle censored data and competing events. Experiments on multiple real-world datasets demonstrate that TraCeR achieves substantial and statistically significant performance improvements over state-of-the-art methods. Furthermore, our evaluation extends beyond discrimination metrics and assesses model calibration, addressing a key oversight in literature.

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arXiv2025

Evaluating pretrained speech embedding systems for dysarthria detection across heterogenous datasets

We present a comprehensive evaluation of pretrained speech embedding systems for the detection of dysarthric speech using existing accessible data. Dysarthric speech datasets are often small and can suffer from recording biases as well as data imbalance. To address these we selected a range of datasets covering related conditions and adopt the use of several cross-validations runs to estimate the chance level. To certify that results are above chance, we compare the distribution of scores across these runs against the distribution of scores of a carefully crafted null hypothesis. In this manner, we evaluate 17 publicly available speech embedding systems across 6 different datasets, reporting the cross-validation performance on each. We also report cross-dataset results derived when training with one particular dataset and testing with another. We observed that within-dataset results vary considerably depending on the dataset, regardless of the embedding used, raising questions about which datasets should be used for benchmarking. We found that cross-dataset accuracy is, as expected, lower than within-dataset, highlighting challenges in the generalization of the systems. These findings have important implications for the clinical validity of systems trained and tested on the same dataset.

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arXiv2025

Age-stratified clustering of multiple long-term conditions

Background: Most people with any long-term condition have multiple long-term conditions, but our understanding of how conditions cluster is limited. Many clustering studies identify clusters in the whole population, but the clusters that occur in people of different ages may be distinct. The aim of this paper was to explore similarities and differences in clusters found in different age-groups. Method: We present a method for finding similar clusters in multiple age-groups, referred to as cluster sets, using Latent Class Analysis (LCA) and Chebyshev distance metric. We analyse a primary care electronic health record (EHR) dataset recording the presence of 40 long-term conditions (LTCs) in 570,355 people aged 40-99 years with at least one of these conditions, analysing in five-year age-groups. Findings: We find that the 600 clusters found separately in 12 age-strata can be summarised by 342 cluster sets with 263 cluster sets only being found in a single age-group (singleton cluster sets), and 79 cluster sets being present in multiple age-groups. We observe that 31 conditions of the 40 conditions studied appear in cluster sets with the respective condition being the only condition present with a very high prevalence of more than 0.9 whereas the remaining cluster sets typically contain two to four conditions present with a high prevalence of more than 0.7. Interpretation: Multimorbidity profiles in different age-groups are often distinct (singleton cluster sets observed only in that age-group), but similar clusters with small variations in their composition are also found in multiple age-groups. This demonstrates the age dependency of MLTC clusters and presents a case for age-stratified clustering.

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arXiv2025

What do we mean when we say we are clustering multimorbidity?

Clustering multimorbidity has been a global research priority in recent years. Existing studies usually identify these clusters using one of several popular clustering methods and then explore various characteristics of these clusters, e.g., their genetic underpinning or their sociodemographic drivers, as downstream analysis. These studies make several choices during clustering that are often not explicitly acknowledged in the literature, e.g., whether they are clustering conditions or clustering individuals, and thus, they lead to different clustering solutions. We observe that, in general, clustering multimorbidity might mean different things in different studies, and argue that making these choices more explicit and, more importantly, letting the downstream analysis, or the purpose of identifying multimorbidity clusters, guide these choices, might lead to more transparent and operationalizable multimorbidity clusters. In this study, we discuss various purposes of identifying multimorbidity clusters and build a case for how different purposes can justify the different choices in data and methods.

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arXiv2024

Probabilistic Modelling of Multiple Long-Term Condition Onset Times

The co-occurrence of multiple long-term conditions (MLTC), or multimorbidity, in an individual can reduce their lifespan and severely impact their quality of life. Exploring the longitudinal patterns, e.g. clusters, of disease accrual can help better understand the genetic and environmental drivers of multimorbidity, and potentially identify individuals who may benefit from early targeted intervention. We introduce $\textit{probabilistic modelling of onset times}$, or $\texttt{ProMOTe}$, for clustering and forecasting MLTC trajectories. $\texttt{ProMOTe}$ seamlessly learns from incomplete and unreliable disease trajectories that is commonplace in Electronic Health Records but often ignored in existing longitudinal clustering methods. We analyse data from 150,000 individuals in the UK Biobank and identify 50 clusters showing patterns of disease accrual that have also been reported by some recent studies. We further discuss the forecasting capabilities of the model given the history of disease accrual.

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arXiv2024

Frailty or Frailties: Exploring Frailty Index Subdimensions in the English Longitudinal Study of Ageing

Background: Frailty, a state of increased vulnerability to adverse health outcomes, has garnered significant attention in research and clinical practice. Existing constructs aggregate clinical features or health deficits into a single score. While simple and interpretable, this approach may overlook the complexity of frailty and not capture the full range of variation between individuals. Methods: Exploratory factor analysis was used to infer latent dimensions of a frailty index constructed using survey data from the English Longitudinal Study of Ageing (ELSA), wave 9. The dataset included 58 self-reported health deficits in a representative sample of community-dwelling adults aged 65+ (N = 4971). Deficits encompassed chronic disease, general health status, mobility, independence with activities of daily living, psychological wellbeing, memory and cognition. Multiple linear regression examined associations with CASP-19 quality of life scores. Results: Factor analysis revealed four frailty subdimensions. Based on the component deficits with the highest loading values, these factors were labelled "Mobility Impairment and Physical Morbidity", "Difficulties in Daily Activities", "Mental Health" and "Disorientation in Time". The four subdimensions were a better predictor of quality of life than frailty index scores. Conclusions: Distinct subdimensions of frailty can be identified from standard index scores. A decomposed approach to understanding frailty has potential to provide a more nuanced understanding of an individual's state of health across multiple deficits.

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arXiv2021

Census-Independent Population Estimation using Representation Learning

Knowledge of population distribution is critical for building infrastructure, distributing resources, and monitoring the progress of sustainable development goals. Although censuses can provide this information, they are typically conducted every ten years with some countries having forgone the process for several decades. Population can change in the intercensal period due to rapid migration, development, urbanisation, natural disasters, and conflicts. Census-independent population estimation approaches using alternative data sources, such as satellite imagery, have shown promise in providing frequent and reliable population estimates locally. Existing approaches, however, require significant human supervision, for example annotating buildings and accessing various public datasets, and therefore, are not easily reproducible. We explore recent representation learning approaches, and assess the transferability of representations to population estimation in Mozambique. Using representation learning reduces required human supervision, since features are extracted automatically, making the process of population estimation more sustainable and likely to be transferable to other regions or countries. We compare the resulting population estimates to existing population products from GRID3, Facebook (HRSL) and WorldPop. We observe that our approach matches the most accurate of these maps, and is interpretable in the sense that it recognises built-up areas to be an informative indicator of population.

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arXiv2021

Towards Sustainable Census Independent Population Estimation in Mozambique

Reliable and frequent population estimation is key for making policies around vaccination and planning infrastructure delivery. Since censuses lack the spatio-temporal resolution required for these tasks, census-independent approaches, using remote sensing and microcensus data, have become popular. We estimate intercensal population count in two pilot districts in Mozambique. To encourage sustainability, we assess the feasibility of using publicly available datasets to estimate population. We also explore transfer learning with existing annotated datasets for predicting building footprints, and training with additional `dot' annotations from regions of interest to enhance these estimations. We observe that population predictions improve when using footprint area estimated with this approach versus only publicly available features.

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arXiv2017

Model Criticism in Latent Space

Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e.g. Gelman, Carlin, Stern, and Rubin [2004, p. 165]. This paper presents a method for latent variable models by pulling back the data into the space of latent variables, and carrying out model criticism in that space. Making use of a model's structure enables a more direct assessment of the assumptions made in the prior and likelihood. We demonstrate the method with examples of model criticism in latent space applied to factor analysis, linear dynamical systems and Gaussian processes.

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arXiv2015

Modelling-based experiment retrieval: A case study with gene expression clustering

Motivation: Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art of keyword searches from annotations, methods for content-based retrieval have been proposed. In the context of gene expression experiments, most methods retrieve gene expression profiles, requiring each experiment to be expressed as a single profile, typically of case vs. control. A more general, recently suggested alternative is to retrieve experiments whose models are good for modelling the query dataset. However, for very noisy and high-dimensional query data, this retrieval criterion turns out to be very noisy as well. Results: We propose doing retrieval using a denoised model of the query dataset, instead of the original noisy dataset itself. To this end, we introduce a general probabilistic framework, where each experiment is modelled separately and the retrieval is done by finding related models. For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples. The suggested metric for retrieval using clusterings is the normalized information distance. Empirical results finally suggest that inference for the full probabilistic model can be approximated with good performance using computationally faster heuristic clustering approaches (e.g. $k$-means). The method is highly scalable and straightforward to apply to construct a general-purpose gene expression experiment retrieval method. Availability: The method can be implemented using standard clustering algorithms and normalized information distance, available in many statistical software packages.

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arXiv2014

Retrieval of Experiments by Efficient Estimation of Marginal Likelihood

We study the task of retrieving relevant experiments given a query experiment. By experiment, we mean a collection of measurements from a set of `covariates' and the associated `outcomes'. While similar experiments can be retrieved by comparing available `annotations', this approach ignores the valuable information available in the measurements themselves. To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements. We argue that such a metric is a sensible measure of similarity between two experiments since it permits inclusion of experiment-specific prior knowledge. However, accurate models are often not analytical, and one must resort to storing posterior samples which demands considerable resources. Therefore, we study strategies to select informative posterior samples to reduce the computational load while maintaining the retrieval performance. We demonstrate the efficacy of our approach on simulated data with simple linear regression as the models, and real world datasets.

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arXiv2013

Probabilistic Archetypal Analysis

Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.

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arXiv2013

Bayesian Extensions of Kernel Least Mean Squares

The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that "kernelizes" the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted as an approximate Bayesian filtering method. This allows us to systematically develop extensions of the KLMS by modifying the underlying state-space and observation models. The resulting extensions introduce many desirable properties such as "forgetting", and the ability to learn from discrete data, while retaining the computational simplicity and time complexity of the original algorithm.

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arXiv2013

Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space

We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data. Since experiments are inherently noisy and databases ever accumulating, we argue that a retrieval engine should possess two particular characteristics. First, it should compare models learnt from the experiments rather than the raw measurements themselves: this allows incorporating experiment-specific prior knowledge to suppress noise effects and focus on what is important. Second, it should be updated sequentially from newly published experiments, without explicitly storing either the measurements or the models, which is critical for saving storage space and protecting data privacy: this promotes life long learning. We formulate the retrieval as a ``supermodelling'' problem, of sequentially learning a model of the set of posterior distributions, represented as sets of MCMC samples, and suggest the use of Particle-Learning-based sequential Dirichlet process mixture (DPM) for this purpose. The relevance measure for retrieval is derived from the supermodel through the mixture representation. We demonstrate the performance of the proposed retrieval method on simulated data and molecular biological experiments.

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arXiv2013

Exploration and retrieval of whole-metagenome sequencing samples

Over the recent years, the field of whole metagenome shotgun sequencing has witnessed significant growth due to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster, and with better coverage than before. This technical advancement has initiated the trend of sequencing multiple samples in different conditions or environments to explore the similarities and dissimilarities of the microbial communities. Examples include the human microbiome project and various studies of the human intestinal tract. With the availability of ever larger databases of such measurements, finding samples similar to a given query sample is becoming a central operation. In this paper, we develop a content-based exploration and retrieval method for whole metagenome sequencing samples. We apply a distributed string mining framework to efficiently extract all informative sequence $k$-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples. We evaluate the performance of the proposed approach on two human gut metagenome data sets as well as human microbiome project metagenomic samples. We observe significant enrichment for diseased gut samples in results of queries with another diseased sample and very high accuracy in discriminating between different body sites even though the method is unsupervised. A software implementation of the DSM framework is available at https://github.com/HIITMetagenomics/dsm-framework

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arXiv2013

Kernel methods on spike train space for neuroscience: a tutorial

Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal processing experts. This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed. The presentation incorporates simple mathematical analogies and convincing practical examples in an attempt to show the yet unexplored potential of positive definite functions to quantify point processes. It also provides a detailed overview of the current state of the art and future challenges with the hope of engaging the readers in active participation.

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