A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions

• URL: http://arxiv.org/abs/2409.01444v3
• Date: Thu, 24 Apr 2025 14:28:10 GMT
• Title: A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions
• Authors: Wouter A. C. van Amsterdam,
• Abstract summary: This work introduces a novel framework that differentiates the effects of case-mix shifts on discrimination and calibration based on the causal direction of the prediction task.<n>A simulation study and empirical validation using cardiovascular disease prediction models demonstrate the implications of this framework.
• Score: 0.9065034043031668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prediction models need reliable predictive performance as they inform clinical decisions, aiding in diagnosis, prognosis, and treatment planning. The predictive performance of these models is typically assessed through discrimination and calibration. Changes in the distribution of the data impact model performance and there may be important changes between a model's current application and when and where its performance was last evaluated. In health-care, a typical change is a shift in case-mix. For example, for cardiovascular risk management, a general practitioner sees a different mix of patients than a specialist in a tertiary hospital. This work introduces a novel framework that differentiates the effects of case-mix shifts on discrimination and calibration based on the causal direction of the prediction task. When prediction is in the causal direction (often the case for prognosis predictions), calibration remains stable under case-mix shifts, while discrimination does not. Conversely, when predicting in the anti-causal direction (often with diagnosis predictions), discrimination remains stable, but calibration does not. A simulation study and empirical validation using cardiovascular disease prediction models demonstrate the implications of this framework. The causal case-mix framework provides insights for developing, evaluating and deploying prediction models across different clinical settings, emphasizing the importance of understanding the causal structure of the prediction task.

Related papers

• Evidential time-to-event prediction with calibrated uncertainty quantification [12.446406577462069]
  Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. We propose an evidential regression model specifically designed for time-to-event prediction. We show that our model delivers both accurate and reliable performance, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-12T15:06:04Z)
• Achieving Fairness in Predictive Process Analytics via Adversarial Learning [50.31323204077591]
  This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value.
  arXiv  Detail & Related papers  (2024-10-03T15:56:03Z)
• Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts [2.309018557701645]
  This paper evaluates whether predictive uncertainty estimation adds robustness to deep learning-based diagnostic decision-making systems. We first investigate three popular methods for improving predictive uncertainty: Monte Carlo dropout, deep ensemble, and few-shot learning on lung adenocarcinoma classification as a primary disease in whole slide images.
  arXiv  Detail & Related papers  (2024-08-15T21:49:43Z)
• When accurate prediction models yield harmful self-fulfilling prophecies [16.304160143287366]
  We show that using prediction models for decision making can lead to harmful decisions. Our main result is a formal characterization of a set of such prediction models. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models.
  arXiv  Detail & Related papers  (2023-12-02T19:39:50Z)
• A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission [0.0]
  We propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions. We evaluate the performance of the proposed method on a clinical dataset using accuracy and fairness measures.
  arXiv  Detail & Related papers  (2022-06-13T16:07:25Z)
• Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning [31.97813934144506]
  We introduce a new representation learning framework, which considers the provision of counterfactual explanations as an embedded property of the risk model. Our results suggest that our proposed framework has the potential to help researchers and clinicians improve personalised care.
  arXiv  Detail & Related papers  (2022-05-15T09:41:16Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Bayesian prognostic covariate adjustment [59.75318183140857]
  Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
  arXiv  Detail & Related papers  (2020-12-24T05:19:03Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making [68.19284302320146]
  We carry out user studies to assess how people with differing levels of expertise respond to different types of predictive uncertainty. We found that showing posterior predictive distributions led to smaller disagreements with the ML model's predictions. This suggests that posterior predictive distributions can potentially serve as useful decision aids which should be used with caution and take into account the type of distribution and the expertise of the human.
  arXiv  Detail & Related papers  (2020-11-12T02:23:53Z)
• Uncertainty estimation for classification and risk prediction on medical tabular data [0.0]
  This work advances the understanding of uncertainty estimation for classification and risk prediction on medical data. In a data-scarce field such as healthcare, the ability to measure the uncertainty of a model's prediction could potentially lead to improved effectiveness of decision support tools.
  arXiv  Detail & Related papers  (2020-04-13T08:46:41Z)
• Learning to Predict Error for MRI Reconstruction [67.76632988696943]
  We demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error. We propose a novel method that estimates the target labels and magnitude of the prediction error in two steps.
  arXiv  Detail & Related papers  (2020-02-13T15:55:32Z)

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.