  Survival analysis plays a crucial role in many healthcare decisions, where
the risk prediction for the events of interest can support an informative
outlook for a patient's medical journey. Given the existence of data censoring,
an effective way of survival analysis is to enforce the pairwise temporal
concordance between censored and observed data, aiming to utilize the time
interval before censoring as partially observed time-to-event labels for
supervised learning. Although existing studies mostly employed ranking methods
to pursue an ordering objective, contrastive methods which learn a
discriminative embedding by having data contrast against each other, have not
been explored thoroughly for survival analysis. Therefore, in this paper, we
propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv)
analysis framework that utilizes survival durations from both censored and
observed data to define temporal distinctiveness and construct negative sample
pairs with adjustable hardness for contrastive learning. Specifically, we first
use an ontological encoder and a sequential self-attention encoder to represent
the longitudinal EHR data with rich contexts. Second, we design a temporal
contrastive loss to capture varying survival durations in a supervised setting
through a hardness-aware negative sampling mechanism. Last, we incorporate the
contrastive task into the time-to-event predictive task with multiple loss
components. We conduct extensive experiments using a large EHR dataset to
forecast the risk of hospitalized patients who are in danger of developing
acute kidney injury (AKI), a critical and urgent medical condition. The
effectiveness and explainability of the proposed model are validated through
comprehensive quantitative and qualitative studies.
