Early warning system scores for clinical deterioration in hospitalized patients: a systematic review.

By M. E. Smith, J. Chiovaro, M. O’Neil, D. Kansagara, Ana R. Quiñones, M. Freeman, M. Motu’apuaka, C. Slatore. 2014

337 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Surgery

How researchers see this paper

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

In Cardiology and Cardiovascular Medicine

1. Greg S. Corrado
2. Michael D. Howell
3. Michaela Hardt
4. Alvin Rajkomar

In Epidemiology

1. Greg S. Corrado
2. Michael D. Howell
3. Atul J. Butte
4. Michaela Hardt
5. Alvin Rajkomar

In Surgery

1. Emiliano Schena
2. Carlo Massaroni
3. Massimo Sacchetti
4. Andrea Nicolò

Citations in the corpus

• AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, neutral): “Risk-scoring models are sparse models with integer point scores, which are used pervasively throughout medicine for risk stratification [1].”
• Scalable and accurate deep learning with electronic health records (2018, methodology): “We used recent literature reviews to select commonly used variables for each task [58, 51, 39].”
• The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise (2020, background): “However, recent systematic reviews have found little evidence of any clinical effectiveness of the early warning scores commonly used in the clinical setting [110,297,298].”