Document Type : Original Article

Authors

• Solmaz norouzi ¹
• Mohammad Asghari Jafarabadi ²
• Hossein Khormaei ³
• Ebrahim Hajizadeh ⁴
• nasim naderi ⁵

¹ University of Qazvin, Zanjan University of Medical Sciences

² Biostatistics Unit, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, 3004, Australia. Department of Psychiatry, School of Clinical Sciences, Faculty

³ Department of Electrical Engineering, National University of Skills (NUS), Tehran, Iran.

⁴ Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.

⁵ Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.

Abstract

Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average.

Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average.

Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average.

Keywords

• Keywords—Deep Learning
• prediction
• survival analysis
• heart failure

Main Subjects

• Artificial Intelligence, Robotics, Cognitive Computing