Review of Language Models for Survival Analysis

Vincent Jeanselme, Nikita Agarwal, Chen Wang

Track: Traditional track

Keywords: Survival analysis; Risk Estimate; Review

Abstract:

By learning statistical relations between words, Large Language Models (LLMs) have presented the capacity to capture meaningful representations for tasks beyond the ones they were trained for. LLMs' widespread accessibility and flexibility have attracted interest among medical practitioners, leading to extensive exploration of their utility in medical prognostic and diagnostic applications. Our work reviews LLMs' use for survival analysis, a statistical tool for estimating the time to an event of interest and, consequently, medical risk. We propose a classification of LLMs' modelling strategies and adaptations to survival analysis, detailing their limitations and strengths. Due to the absence of standardised guidelines in the literature, we introduce a framework to assess the efficacy of diverse LLM strategies for survival analysis.

Presentation And Attendance Policy: I have read and agree with the symposium's policy on behalf of myself and my co-authors.

Ethics Board Approval: No, our research does not involve datasets that need IRB approval or its equivalent.

Data And Code Availability: Yes, we will make data and code available upon acceptance.

Primary Area: General machine learning for healthcare (i.e. none of the above)

Student First Author: Yes, the primary author of the manuscript is a student.

Submission Number: 23