Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery

Abstract

Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability — variations in measurements that are not due to disease subtype — which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments.

Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children

“…13 In general, these approaches have used a single time (eg, initial presentation) to define the different phenotypes and have not investigated the dynamic patterns of illness, despite evidence that this may be important in phenotyping. [19][20][21][22] Furthermore, a single snapshot approach does not account for the fact that patients may present to a critical care setting at different points in their illness and that organ dysfunctions tend to peak between days 1 and 3 of admission. 23 In this study we aimed to derive, validate, and characterize novel phenotypes of MODS in critically ill children using a data-driven approach based on the type, severity, and trajectory of organ dysfunctions in the acute phase of critical illness.…”

Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

“…Due to the ability of exact inference, GP based model are widely used in time series regression and forecasting tasks, where time stamps are modeled as the input of GP and observations are modeled through the predicted mean function of the time series (Stegle et al 2008;Clifton et al 2013;Lasko, Denny, and Levy 2013;Liu and Hauskrecht 2014).…”

“…The majority of existing work about clinical time series forecasting models each clinical time series separately (Marlin et al 2012;Clifton et al 2013;Lasko, Denny, and Levy 2013;Liu and Hauskrecht 2014;Schulam, Wigley, and Saria 2015) which does not allow one to represent dependences among the different time series. Our model deals with multivariate data and aims to capture interactions among all variables and their dynamics.…”

“…With a wide adoption and availability of electronic health records (EHRs), the development of models of clinical multivariate time series (MTS) and tools for their analysis is becoming increasingly important for meaningful applications of EHRs in computer-based patient monitoring, adverse event detection, and improved patient management. (Bellazzi et al 2000;Clifton et al 2013;Lasko, Denny, and Levy 2013;Liu, Wu, and Hauskrecht 2013;Schulam, Wigley, and Saria 2015;Ghassemi et al 2015;Durichen et al 2015).…”

Clinical Subtyping of Ménière's Disease

“…Clinical subtyping has offered greater understanding of many other disease processes 1 ; Parkinson's disease is a good example 2 . Like Ménière's disease, Parkinson's disease presents in a variable manner, and ongoing work to define clinical subtypes has been proposed to be important in identifying homogeneous groups with strong clinical, pathologic, and genetic coherence, 3 leading to a better understanding of the involved biological pathways and ultimately to tailored treatment strategies and prognostic information.…”