Machine Learning, Predictive Analytics, and Clinical Practice: Can the Past Inform the Present?

[1]  A. Stiggelbout,et al.  Shared Decision Making and the Importance of Time. , 2019, JAMA.

[2]  Daniel E Forman,et al.  2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2019, Journal of the American College of Cardiology.

[3]  Jennifer G. Robinson,et al.  Influence of Cardiovascular Risk Communication Tools and Presentation Formats on Patient Perceptions and Preferences , 2018, JAMA cardiology.

[4]  J. Kai,et al.  Can machine-learning improve cardiovascular risk prediction using routine clinical data? , 2017, PloS one.

[5]  B. Goldstein,et al.  Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges , 2016, European heart journal.

[6]  Michael J Pencina,et al.  Risk Prediction With Electronic Health Records: The Importance of Model Validation and Clinical Context. , 2016, JAMA cardiology.

[7]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. , 2014, Circulation.

[8]  Robert Califf,et al.  Value of the History and Physical in Identifying Patients at Increased Risk for Coronary Artery Disease , 1993, Annals of Internal Medicine.

[9]  F. Harrell,et al.  Predicting outcome in coronary disease. Statistical models versus expert clinicians. , 1986, The American journal of medicine.

[10]  R M Califf,et al.  The doctor and the computer. , 1981, The Western journal of medicine.