Recent trends in Survival analysis using Deep Learning in Medical Science: Current Perspective and Future Direction

Volume 20 No 9 (2022)

Palak Kaushal, Dr. Shailendra Singh, Dr. Dharam Vir

DOI: 10.14704/nq.2022.20.9.NQ44384

Abstract

Survival Analysis manifests a profound effect on healthcare services. It is the analysis of a dichotomous event, i.e., occurrence or non-occurrence of the event over a period of time. While the traditional survival analysis approaches work with some pre-assumptions and do not include time-sequential data, recently developed machine learning based systems can capture non-linearity and involve time- dependent features from multiple visits. The article highlights the ongoing research demonstrating that the machine learning-based survival analysis approaches have consistently improved compared to the state-of-art statistical survival analysis approaches.

Keywords

Survival Analysis; Deep Learning; Intelligent Healthcare systems, Neural Networks

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