PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models

• URL: http://arxiv.org/abs/2509.22673v1
• Date: Sat, 13 Sep 2025 18:09:14 GMT
• Title: PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
• Authors: Thalea Schlender, Catharina J. A. Romme, Yvette M. van der Linden, Luc R. C. W. van Lonkhuijzen, Peter A. N. Bosman, Tanja Alderliesten,
• Abstract summary: Survival analysis is central to clinical research, informing patient prognoses, guiding treatment decisions, and optimising resource allocation.<n>For these models to be relevant in healthcare, predictions must be traceable to patient-specific characteristics.<n>Traditional survival models often fail to capture non-linear interactions, while modern deep learning approaches are limited by poor interpretability.<n>We propose a Pipeline for Interpretable Survival Analysis (PISA) - a pipeline that provides multiple survival analysis models that trade off complexity and performance.
• Score: 0.9851812512860351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Survival analysis is central to clinical research, informing patient prognoses, guiding treatment decisions, and optimising resource allocation. Accurate time-to-event predictions not only improve quality of life but also reveal risk factors that shape clinical practice. For these models to be relevant in healthcare, interpretability is critical: predictions must be traceable to patient-specific characteristics, and risk factors should be identifiable to generate actionable insights for both clinicians and researchers. Traditional survival models often fail to capture non-linear interactions, while modern deep learning approaches, though powerful, are limited by poor interpretability. We propose a Pipeline for Interpretable Survival Analysis (PISA) - a pipeline that provides multiple survival analysis models that trade off complexity and performance. Using multiple-feature, multi-objective feature engineering, PISA transforms patient characteristics and time-to-event data into multiple survival analysis models, providing valuable insights into the survival prediction task. Crucially, every model is converted into simple patient stratification flowcharts supported by Kaplan-Meier curves, whilst not compromising on performance. While PISA is model-agnostic, we illustrate its flexibility through applications of Cox regression and shallow survival trees, the latter avoiding proportional hazards assumptions. Applied to two clinical benchmark datasets, PISA produced interpretable survival models and intuitive stratification flowcharts whilst achieving state-of-the-art performances. Revisiting a prior departmental study further demonstrated its capacity to automate survival analysis workflows in real-world clinical research.

Related papers

• Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks [47.19194118883552]
  We introduce a multimodal deep learning framework for survival analysis capable of modeling both single and competing risks scenarios.<n>We propose SAMVAE (Survival Analysis Multimodal Variational Autoencoder), a novel deep learning architecture designed for survival prediction.
  arXiv  Detail & Related papers  (2025-07-10T14:29:48Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation [7.698783025721071]
  We propose IMLSP, an Interpretable Multi-Label multi-modal deep Survival Prediction framework for predicting multiple HNC survival outcomes simultaneously. We also present Grad-TEAM, a Gradient-weighted Time-Event Activation Mapping approach specifically developed for deep survival model visual explanation.
  arXiv  Detail & Related papers  (2024-05-09T01:30:04Z)
• SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records [26.66492761632773]
  The accurate prediction of survival times for patients with severe diseases remains a critical challenge despite recent advances in artificial intelligence. This study introduces "SurvTimeSurvival: Survival Analysis On Patients With Multiple Visits/Records"
  arXiv  Detail & Related papers  (2023-11-16T12:30:14Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Dynamic prediction of time to event with survival curves [3.9169188005935927]
  We apply our recently developed counterfactual dynamic survival model (CDSM) to static and longitudinal observational data. We prove that the inflection point of its estimated individual survival curves provides reliable prediction of the patient failure time.
  arXiv  Detail & Related papers  (2021-01-26T12:17:27Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.