Deep-learning in survival analysis
In this paper, hospitalisation duration is modelled using traditional survival model, machine-learning models and deep-learning models. Machine-learning and deep-learning algorithms typically assume that all event of interest are known at the time of modelling. However, hospitalisation duration i...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/148490 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In this paper, hospitalisation duration is modelled using traditional survival model, machine-learning
models and deep-learning models. Machine-learning and deep-learning algorithms typically assume
that all event of interest are known at the time of modelling. However, hospitalisation duration
is a time-to-event data with right-censoring (not all events are known at the time of modelling).
Hence, specific techniques were employed to deal with this inconsistency. Subsequently, the various
models are evaluated using the Concordance-index (C-index). It is a ranking evaluation metrics that
can account for censored observation. The empirical results showed that the deep-learning model is
best in predicting hospitalisation despite the small dataset. This paper can be further improved by
incorporating geo-spatial data in the analysis. |
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