Survival Analysis
Ataraxis advances survival analysis with multimodal models that integrate pathology-derived features with clinical data, estimating how a patient's risk evolves over time rather than producing a single static score. Our models deliver calibrated, time-based probabilities of recurrence or survival that support personalized treatment planning and form the foundation for modeling treatment effects.
Why survival analysis matters
Most machine learning models treat prediction as a simple classification, such as high risk or low risk. Survival analysis goes further, estimating the likelihood of recurrence across specific time horizons such as within 5 years, 10 years, or beyond. A 20% chance of recurrence within 2 years represents a very different clinical situation than a 20% chance over 10 years, and the difference matters for treatment decisions. By accounting for incomplete data, survival analysis provides a richer, more clinically meaningful view of prognosis that better guides treatment intensity, surveillance, and follow-up.
Our models train on diverse real-world datasets spanning multiple institutions and cancer types, capturing how disease risk shifts over time and surfacing temporal patterns that conventional approaches miss. Pathology and clinical signals are trained together so the system learns jointly from both modalities, using time-to-event loss functions including Cox and discrete-time survival models.
By combining the interpretability of survival analysis with the representational power of foundation models, we transform prognosis from a fixed snapshot into a dynamic, patient-specific prediction that adapts to individual biology and clinical context.