On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning [PDF]
Journal of the American Medical Informatics Association 27 (2020)
437-443, 2019 Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the ICU in ...
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Soft Phenotyping for Sepsis via EHR Time-aware Soft Clustering [PDF]
arXiv, 2023 Objective: Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories, identifying sepsis sub ...
arxiv
Detection of sepsis during emergency department triage using machine learning [PDF]
arXiv, 2022 Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Even a few hours of delay in the treatment of sepsis results in increased mortality. Early detection of sepsis during emergency department triage would allow early initiation of lab analysis, antibiotic administration, and other
arxiv
Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients [PDF]
arXiv, 2021 Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the US, while a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning.
arxiv
Multi-Subset Approach to Early Sepsis Prediction [PDF]
arXiv, 2023 Sepsis is a life-threatening organ malfunction caused by the host's inability to fight infection, which can lead to death without proper and immediate treatment. Therefore, early diagnosis and medical treatment of sepsis in critically ill populations at high risk for sepsis and sepsis-associated mortality are vital to providing the patient with rapid ...
arxiv
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data [PDF]
arXiv, 2022 Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response ...
arxiv
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning [PDF]
arXiv, 2021 Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive.
arxiv
Identifying and Analyzing Sepsis States: A Retrospective Study on Patients with Sepsis in ICUs [PDF]
arXiv, 2020 Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost.
arxiv
An Improved Mathematical Model of Sepsis: Modeling, Bifurcation Analysis, and Optimal Control Study for Complex Nonlinear Infectious Disease System [PDF]
arXiv, 2022 Sepsis is a life-threatening medical emergency, which is a major cause of death worldwide and the second highest cause of mortality in the United States. Researching the optimal control treatment or intervention strategy on the comprehensive sepsis system is key in reducing mortality.
arxiv
Sepsis Prediction with Temporal Convolutional Networks [PDF]
arXiv, 2022 We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall under the definition of sepsis at the time of admission.
arxiv