Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

By M. M. R. Andrew Taylor, MD Joseph R. Pare, M. M. M. Arjun K. Venkatesh, MD Mph Hani Mowafi, M. M. Edward R. Melnick, MD William Fleischman, M. M. M. Kennedy Hall. Academic Emergency Medicine, 2016

436 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Emergency Medicine, Epidemiology

View Paper: https://www.semanticscholar.org/paper/970adc0c514bacabd7a6e25e6d6a4141b6fb8f08

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuan-jie Li
2. An-Ding Xu
3. Aozi Feng
4. Yeha Lee
5. Seungwoo Lee
6. Youngnam Lee
7. Tao Huang
8. Wen-Tao Wu

In Emergency Medicine

1. Sameer Quazi

In Epidemiology

1. Sameer Quazi

Citations in the corpus

• Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, background): “The authors demonstrated that the predictive performance of the RF method was superior to that of traditional emergency medicine methods and the methods enabled evaluation of more clinical variables than traditional modelling methods, which subsequently allowed the discovery of clinical variables no”
• An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest (2018, methodology): “In the previous studies, logistic regression and random forest were the most commonly used machine-learning methods and showed better performance than traditional TTSs.32,33 We used the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPR”