ArticleJournal of biomedical informatics2018
Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A scoping review of the clinical application of machine learning in data-driven population segmentation analysis.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Unsupervised probabilistic models for sequential Electronic Health Records.Journal of biomedical informatics · 2022Article
- Methods for Phenotyping Adult Patients in Sepsis and Septic Shock: A Scoping Review.Critical care explorations · 2022Article
- Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data.IEEE journal of biomedical and health informatics · 2022Article
- Early prediction of hypothermia in pediatric intensive care units using machine learning.Frontiers in physiology · 2022Article
- Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2022Article
- Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.Journal of biomedical informatics · 2021Review
- Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen.Critical care (London, England) · 2021Article
- Artificial Intelligence Algorithm with ICD Coding Technology Guided by the Embedded Electronic Medical Record System in Medical Record Information Management.Journal of healthcare engineering · 2021Article
- An evaluation of time series summary statistics as features for clinical prediction tasks.BMC medical informatics and decision making · 2020Article
- Three Data-Driven Phenotypes of Multiple Organ Dysfunction Syndrome Preserved from Early Childhood to Middle Adulthood.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Article
- Big Data and Data Science in Critical Care.Chest · 2018Review
- Emerging Technologies for Molecular Diagnosis of Sepsis.Clinical microbiology reviews · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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