AIMC Topic: Sepsis

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Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical featur...

Identifying infected patients using semi-supervised and transfer learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and t...

Lessons in machine learning model deployment learned from sepsis.

Med (New York, N.Y.)
In three recent and related publications, researchers from Johns Hopkins University and Bayesian Health report results from implementing and prospectively evaluating the Targeted Real-time Early Warning System (TREWS) for sepsis at five hospitals..

Enhancing sepsis management through machine learning techniques: A review.

Medicina intensiva
Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is in...

Exploring Features Contributing to the Early Prediction of Sepsis Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The increasing availability of electronic health records and administrative data and the adoption of computer-based technologies in healthcare have significantly focused on medical informatics. Sepsis is a time-critical condition with high mortality,...

A Machine Learning Understanding of Sepsis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sepsis is a serious cause of morbidity and mortality and yet its pathophysiology remains elusive. Recently, medical and technological advances have helped redefine the criteria for sepsis incidence, which is otherwise poorly understood. With the reco...

A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.

Critical care medicine
OBJECTIVES: The National Early Warning Score, Modified Early Warning Score, and quick Sepsis-related Organ Failure Assessment can predict clinical deterioration. These scores exhibit only moderate performance and are often evaluated using aggregated ...

Towards real-time diagnosis for pediatric sepsis using graph neural network and ensemble methods.

European review for medical and pharmacological sciences
OBJECTIVE: The rapid onset of pediatric sepsis and the short optimal time for resuscitation pose a severe threat to children's health in the ICU. Timely diagnosis and intervention are essential to curing sepsis, but there is a lack of research on the...

Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils.

The Tohoku journal of experimental medicine
Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic anemia, myelodysplastic syndrome, and sepsis. It has been reported that one-fifth of sepsis-infecte...