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Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study

Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study

The neural network architecture and performance are described in detail in the paper by Shashikumar et al [18]. To investigate the dynamics of the derived phenotypes, we examined the trajectory of patients from 3 to 6 hours after ED triage.

Aaron Boussina, Gabriel Wardi, Supreeth Prajwal Shashikumar, Atul Malhotra, Kai Zheng, Shamim Nemati

J Med Internet Res 2023;25:e45614

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study

We used the sepsis prediction model by Shashikumar et al [5] to develop an optimization framework that chooses the model’s classification thresholds to minimize the additional costs from sepsis by the MDCs. In the context of sepsis prediction, classification thresholds determine above which probability the model tags a patient as septic.

Parker Rogers, Aaron E Boussina, Supreeth P Shashikumar, Gabriel Wardi, Christopher A Longhurst, Shamim Nemati

J Med Internet Res 2023;25:e43486