PCI PROVIDES COSTLY MORTALITY REDUCTIONS FOR BREAST CANCER PATIENTS: PROPENSITY SCORE AND MACHINE LEARNING AUGMENTED NATIONALLY REPRESENTATIVE CASE-CONTROL STUDY OF 30 MILLION+ HOSPITALIZATIONS

Autor: Messan Folvi, Konstantinos Marmagkiolis, Logan Hostetter, Jeffrey Chen, Nicolas Palaskas, Cullen Grable, Sophia Lee, Tariq Thannoun, Peter Kim, Nicole Thomason, Natalie Chen, Ananya Yalamanchi, Juan Lopez-Mattei, Siddharth Chauhan, Dominique J. Monlezun, Mehmet Cilingiroglu, Kenneth Hoang, Sarah M. DeSnyder, Rahul Gaiba, Jessica S. Colen, Cezar Iliescu, Monica Tamil, Vivian Okirie
Rok vydání: 2020
Předmět:
Zdroj: Journal of the American College of Cardiology. 75:3625
ISSN: 0735-1097
Popis: Since the optimal management of coronary artery disease (CAD) and breast cancer is unknown, we sought to conduct the first known nationally representative study of mortality and cost effectiveness for breast cancer and percutaneous coronary interventions (PCI). Backward propagation neural network
Databáze: OpenAIRE