Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models

Abstract

The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. We demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.

Emerging Technologies for Molecular Diagnosis of Sepsis

“…This study also attempted to stratify the sepsis syndrome to advance the fundamental understanding of the progression of this disease (199). Several other groups have demonstrated the validity of such algorithms in retrospective studies (204)(205)(206). However, only a few machine learning-based algorithms have been implemented in prospective studies beyond the development phase using retrospective chart review.…”

Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

“…Sepsis has been found to be consisted of several phenotypes, though specific class membership assignments are different across studies [12][13][14][15][16][17][18]. By using clinical trial data from 1696 patients, Gårdlund B and colleagues identified six classes of septic shock [19].…”

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

“…1). Three studies used an unsupervised clustering approach for clustering sepsis patients [31,42,44]. Data from sepsis patients were also used to learn optimal treatment by reinforcement learning [19].…”

“…In contrast to supervised ML models, unsupervised approaches for cluster analysis were used by only three studies identified in our review [31,42]. All three focused on sepsis patients.…”

“…Given the high complexity of these patients, unsupervised methods can provide promising potential to better understand this population. The studies aimed at identifying treatment patterns, clinical phenotypes, and mortality-associated patient groups (methods used: latent Dirichlet allocation [42], partitioning around medoids methods [44], and composite mixture models [31] respectively) ( Fig. 1).…”