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Connection

Frederick Masoudi to Clinical Decision-Making

This is a "connection" page, showing publications Frederick Masoudi has written about Clinical Decision-Making.

 
Connection Strength
 
 
 
0.880
 
  1. Daugherty SL, Blair IV, Havranek EP, Furniss A, Dickinson LM, Karimkhani E, Main DS, Masoudi FA. Implicit Gender Bias and the Use of Cardiovascular Tests Among Cardiologists. J Am Heart Assoc. 2017 Nov 29; 6(12).
    View in: PubMed
    Score: 0.417
  2. Boyd C, Smith CD, Masoudi FA, Blaum CS, Dodson JA, Green AR, Kelley A, Matlock D, Ouellet J, Rich MW, Schoenborn NL, Tinetti ME. Decision Making for Older Adults With Multiple Chronic Conditions: Executive Summary for the American Geriatrics Society Guiding Principles on the Care of Older Adults With Multimorbidity. J Am Geriatr Soc. 2019 04; 67(4):665-673.
    View in: PubMed
    Score: 0.114
  3. Kini V, Peterson PN, Spertus JA, Kennedy KF, Arnold SV, Wasfy JH, Curtis JP, Bradley SM, Amin AP, Ho PM, Masoudi FA. Clinical Model to Predict 90-Day Risk of Readmission After Acute Myocardial Infarction. Circ Cardiovasc Qual Outcomes. 2018 10; 11(10):e004788.
    View in: PubMed
    Score: 0.111
  4. Li X, Li J, Masoudi FA, Spertus JA, Lin Z, Krumholz HM, Jiang L. China PEACE risk estimation tool for in-hospital death from acute myocardial infarction: an early risk classification tree for decisions about fibrinolytic therapy. BMJ Open. 2016 10 24; 6(10):e013355.
    View in: PubMed
    Score: 0.097
  5. Huang C, Li SX, Caraballo C, Masoudi FA, Rumsfeld JS, Spertus JA, Normand ST, Mortazavi BJ, Krumholz HM. Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning. Circ Cardiovasc Qual Outcomes. 2021 10; 14(10):e007526.
    View in: PubMed
    Score: 0.034
  6. Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLoS Med. 2018 11; 15(11):e1002703.
    View in: PubMed
    Score: 0.028
  7. Wasfy JH, Kennedy KF, Masoudi FA, Ferris TG, Arnold SV, Kini V, Peterson P, Curtis JP, Amin AP, Bradley SM, French WJ, Messenger J, Ho PM, Spertus JA. Predicting Length of Stay and the Need for Postacute Care After Acute Myocardial Infarction to Improve Healthcare Efficiency. Circ Cardiovasc Qual Outcomes. 2018 09; 11(9):e004635.
    View in: PubMed
    Score: 0.027
  8. Yancy CW, Januzzi JL, Allen LA, Butler J, Davis LL, Fonarow GC, Ibrahim NE, Jessup M, Lindenfeld J, Maddox TM, Masoudi FA, Motiwala SR, Patterson JH, Walsh MN, Wasserman A. 2017 ACC Expert Consensus Decision Pathway for Optimization of Heart Failure Treatment: Answers to 10 Pivotal Issues About Heart Failure With Reduced Ejection Fraction: A Report of the American College of Cardiology Task Force on Expert Consensus Decision Pathways. J Am Coll Cardiol. 2018 01 16; 71(2):201-230.
    View in: PubMed
    Score: 0.026
  9. Mathew JS, Marzec LN, Kennedy KF, Jones PG, Varosy PD, Masoudi FA, Maddox TM, Allen LA. Atrial Fibrillation in Heart Failure US Ambulatory Cardiology Practices and the Potential for Uptake of Catheter Ablation: An National Cardiovascular Data Registry (NCDR®) Research to Practice (R2P) Project. J Am Heart Assoc. 2017 Aug 11; 6(8).
    View in: PubMed
    Score: 0.026
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.

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