CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction

• URL: http://arxiv.org/abs/2308.09735v2
• Date: Fri, 22 Sep 2023 05:54:02 GMT
• Title: CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction
• Authors: Zhoujian Sun, Wenzhuo Zhang, Zhengxing Huang, Nai Ding, Cheng Luo
• Abstract summary: We propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. CTP combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories. We evaluate the performance of the model using simulated and real medical datasets.
• Score: 12.282670150417953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Non-communicable disease is the leading cause of death, emphasizing the need for accurate prediction of disease progression and informed clinical decision-making. Machine learning (ML) models have shown promise in this domain by capturing non-linear patterns within patient features. However, existing ML-based models cannot provide causal interpretable predictions and estimate treatment effects, limiting their decision-making perspective. In this study, we propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. The CTP model combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories and uncover causal relationships between features. By incorporating a causal graph into the prediction process, CTP ensures that ancestor features are not influenced by the treatment of descendant features, thereby enhancing the interpretability of the model. By estimating the bounds of treatment effects, even in the presence of unmeasured confounders, the CTP provides valuable insights for clinical decision-making. We evaluate the performance of the CTP using simulated and real medical datasets. Experimental results demonstrate that our model achieves satisfactory performance, highlighting its potential to assist clinical decisions. Source code is in \href{https://github.com/DanielSun94/CFPA}{here}.

Related papers

• Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database [0.9055332067000195]
  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. predictive models for lung metastasis inHCC remain limited in scope and clinical applicability. We develop and validate an end-to-end machine learning pipeline using data from the Surveillance, Epidemiology, and End Results (SEER) database.
  arXiv  Detail & Related papers  (2025-01-20T20:06:31Z)
• Physics-informed deep learning for infectious disease forecasting [2.938382489367582]
  We propose a new infectious disease forecasting model based on physics-informed neural networks (PINNs) The proposed PINN model incorporates dynamical systems representations of disease transmission into the loss function. Predictions of PINN model on the number of cases, deaths, and hospitalizations are consistent with existing benchmarks.
  arXiv  Detail & Related papers  (2025-01-16T05:07:05Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction [29.415616701032604]
  Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals. We propose a conditional score-based diffusion model to generate CTh trajectories with the given baseline information. Our model has a near-zero bias with narrow confidential 95% interval compared to the ground-truth CTh in 6-36 months.
  arXiv  Detail & Related papers  (2024-03-11T17:26:18Z)
• Deep Stable Representation Learning on Electronic Health Records [8.256340233221112]
  Causal Healthcare Embedding (CHE) aims at eliminating the spurious statistical relationship by removing the dependencies between diagnoses and procedures. Our proposed CHE method can be used as a flexible plug-and-play module that can enhance existing deep learning models on EHR.
  arXiv  Detail & Related papers  (2022-09-03T04:10:45Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• What Do You See in this Patient? Behavioral Testing of Clinical NLP Models [69.09570726777817]
  We introduce an extendable testing framework that evaluates the behavior of clinical outcome models regarding changes of the input. We show that model behavior varies drastically even when fine-tuned on the same data and that allegedly best-performing models have not always learned the most medically plausible patterns.
  arXiv  Detail & Related papers  (2021-11-30T15:52:04Z)
• 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)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs [15.339137501579087]
  We formulated the disease prediction task as a random walk along a knowledge graph (KG) We build a KG to record relationships between diseases and risk factors according to validated medical knowledge. The trajectory generated by the object represents an interpretable disease progression path of the given patient.
  arXiv  Detail & Related papers  (2020-10-16T10:46:28Z)

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.