Deep Learning Frailty Model for Heart Failure Survival Prediction | ||
| Journal of Innovations in Computer Science and Engineering (JICSE) | ||
| دوره 3، شماره 1، شهریور 2025، صفحه 48-52 اصل مقاله (458.08 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jicse.2025.241654.1085 | ||
| نویسندگان | ||
| Solmaz norouzi* 1؛ Hossein Khormaei2؛ Ebrahim Hajizadeh3؛ nasim naderi4 | ||
| 1University of Qazvin, Zanjan University of Medical Sciences | ||
| 2Department of Electrical Engineering, National University of Skills (NUS), Tehran, Iran. | ||
| 3Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. | ||
| 4Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Keywords—Deep Learning؛ prediction؛ survival analysis؛ heart failure | ||
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