The TIMI risk score for unstable angina/non-ST elevation MI: A method for prognostication and therapeutic decision making.

By E. Antman, M. Cohen, P. Bernink, C. Mccabe, T. Horácek, G. Papuchis, B. Mautner, R. Corbalán, D. Radley, E. Braunwald. 2000

3,424 citations recorded by the source corpus.

Subfields: Medical & Health AI

How researchers see this paper

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

In Medical & Health AI

Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Since then, many scoring systems have been designed for healthcare (Knaus et al., 1981, 1985, 1991; Bone et al., 1992; Le Gall et al., 1993; Antman et al., 2000; Gage et al., 2001; Moreno et al., 2005; Six et al., 2008; Weathers et al., 2013; Than et al., 2014).”
Learning Optimized Risk Scores (2016, methodology): “The TIMI Risk Score of Antman et al. (2000), for example, was built as follows: 1.”
Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “, 1997); • TIMI, to assess the risk of death and ischemic events (Antman et al., 2000); • SIRS, to detect system inflammatory response syndrome (Bone et al., 1992); • Ranson Criteria for acute pancreatitis (Ranson et al., 1974); • Light’s Criteria for pleural effusions (Light et al., 1972). All of t”
Falling Rule Lists (2014, methodology): “These manually-created risk assessment tools are used in possibly every hospital; e.g., the TIMI scores, CHADS 2 score, Apache scores, and the Ranson score, to name a few (Antman et al. , 2000; Morrow et al. , 2000; Gage et al. , 2001; Knaus et al. , 1981, 1985, 1991; Ranson et al. , 1974).”
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection (2014, methodology): “The thrombolysis in myocardial infarction (TIMI) [5] score and the Global Registry of Acute Coronary Events (GRACE) [6] score were developed to predict the risk of death, reinfarction and revascularization.”
A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. (2004, background): “Length of hospital stay, median (IQR), d Patients who died 8 (5-12) 6 (3-10) NA Patients discharged alive 6 (3-10) 6 (3-9) NA”
The fused lasso penalty for learning interpretable medical scoring systems (2017, neutral): “Some other widely used medical scores are SAPS I, II, and III [9], [17] and APACHE I, II, III to assess intensive care units mortality risks [13], CHADS2 to assess the risk of stroke [8]; TIMI to estimate the risk of death of ischemic events [1].”