Validation of Clinical Classification Schemes for Predicting Stroke: Results From the National Registry of Atrial Fibrillation

By B. Gage, A. Waterman, W. Shannon, M. Boechler, M. Rich, M. Radford. 2001

3,080 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Internal Medicine, 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 Cardiology and Cardiovascular Medicine

1. Rahul Wadke
2. David Madigan
3. Benjamin Letham
4. Stefano Tracà
5. Berk Ustun
6. Tyler H. McCormick
7. Cynthia Rudin

In Epidemiology

1. Rahul Wadke
2. David Madigan
3. Benjamin Letham
4. Stefano Tracà
5. Berk Ustun
6. Tyler H. McCormick
7. Cynthia Rudin

In Internal Medicine

1. Rahul Wadke
2. David Madigan
3. Benjamin Letham
4. Berk Ustun
5. Tyler H. McCormick
6. Cynthia Rudin

In Medical & Health AI

1. Cynthia Rudin
2. Berk Ustun
3. Benjamin Letham
4. Stefano Tracà
5. David Madigan
6. Lesia Semenova
7. Chudi Zhong
8. Zhi Chen

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).”
• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, methodology): “…et al. (2000)], Apache II score for infant mortality in the ICU [Knaus et al. (1985)], the CURB-65 score for predicting mortality in community-acquired pneumonia [Lim et al. (2003)] and the CHADS2 score [Gage et al. (2001)] are examples of interpretable predictive models that are very widely used.”
• Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, neutral): “For instance, the CHADS2 score (Gage et al. 2001, Letham et al. 2012) is widely used in medicine to predict the risk of stroke in patients with atrial brillation based on ve di erent symptoms of the patient, whereas the extended CHA2DS2 VASc score (Lip et al. 2010) includes three additional risk…”
• Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “…I, II and III, to assess ICU mortality risk (Knaus et al., 1981, 1985, 1991); • CHADS2, to assess the risk of stroke in patients with atrial fibrillation (Gage et al., 2001); • Wells Criteria for pulmonary embolisms (Wells et al., 2000) and deep vein thrombosis (Wells et al., 1997); • TIMI, to…”
• 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).”
• Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice (2018, background): “2% Figure 1 CHADS2 score to assess stroke risk (19).”