A targeted real-time early warning score (TREWScore) for septic shock

By Katharine Henry, D. Hager, Peter J. Pronovost, Suchi Saria. 2015

588 citations recorded by the source corpus.

Subfields: Epidemiology, Family Practice, Surgery, Medical & Health AI

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “One example is personalized medicine, where we may wish to build a model of a patient health state through a multitude of data sources, such as electronic health records and genetic information [66], [109].”
Towards Causal Representation Learning (2021, background): “One example is personalized medicine, where we may wish to build a model of a patient health state through a multitude of data sources, like electronic health records and genetic information [65, 108].”
Learning to Diagnose with LSTM Recurrent Neural Networks (2015, background): “…of research applies machine learning to temporal clinical data for tasks including artifact removal (Aleks et al., 2009; Quinn et al., 2009), early detection and prediction (Stanculescu et al., 2014a; Henry et al., 2015), and clustering and subtyping (Marlin et al., 2012; Schulam et al., 2015).”
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction (2017, methodology): “Following the approach of [18], we treat patients who received treatment and then developed septic shock as interval-censored because the exact time of shock onset could be at any time between the time of treatment and the observed shock onset time.”
External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. (2021, unknown): “Depression 8140 (21) 7478 (21) 662 (26) <.”
Do no harm: a roadmap for responsible machine learning for health care (2019, background): “For example, in deploying an early-warning system for sepsis, an ML-based system requires a novel workflow, such as presenting a display that indicates why the model generated an alert, to facilitate evaluation 18 .”
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series (2016, methodology): “However, our methods can be applied to any predictive modeling problem involving sequence data and missing values, such as early prediction of sepsis (Henry et al., 2015) or real-time risk modeling (Wiens et al., 2012).”
Learning (predictive) risk scores in the presence of censoring due to interventions (2015, background): “Prior Art: Historically, severity scores have been designed in a number of different ways (Ghanem-Zoubi et al, 2011).”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, methodology): “NLP has enabled the automated translation of doctor–patient interactions to notes 5,104,105 , the summarization of clinical notes 106 , the captioning of medical images 107,108 and the prediction of disease progression 6,7 .”
Artificial Intelligence and Surgical Decision-Making (2019, background): “9,21,27,32 Each machine learning algorithm has distinct advantages and disadvantages for different tasks such that performance depends on fit between algorithm and task.”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, methodology): “AI’ (243,244), for analyzing EHR of cancer patients and recommend treatments and drug doses; TREWScore (245,246,247,248) for analyzing EHR for predicting septic shock risk and analyze CT brain scans for three-dimensional convolutional neural network neurological disorder classification; ‘PowerLook D”