An explainable Transformer-based deep learning model for the prediction of incident heart failure

• URL: http://arxiv.org/abs/2101.11359v1
• Date: Wed, 27 Jan 2021 12:45:15 GMT
• Title: An explainable Transformer-based deep learning model for the prediction of incident heart failure
• Authors: Shishir Rao, Yikuan Li, Rema Ramakrishnan, Abdelaali Hassaine, Dexter Canoy, John Cleland, Thomas Lukasiewicz, Gholamreza Salimi-Khorshidi, Kazem Rahimi
• Abstract summary: We developed a novel Transformer deep-learning model for prediction of incident heart failure involving 100,071 patients. The model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve. The importance of contextualised medical information was revealed in sensitivity analyses.
• Score: 22.513476932615845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We developed a novel Transformer deep-learning model for more accurate and yet explainable prediction of incident heart failure involving 100,071 patients from longitudinal linked electronic health records across the UK. On internal 5-fold cross validation and held-out external validation, our model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve, respectively and outperformed existing deep learning models. Predictor groups included all community and hospital diagnoses and medications contextualised within the age and calendar year for each patient's clinical encounter. The importance of contextualised medical information was revealed in a number of sensitivity analyses, and our perturbation method provided a way of identifying factors contributing to risk. Many of the identified risk factors were consistent with existing knowledge from clinical and epidemiological research but several new associations were revealed which had not been considered in expert-driven risk prediction models.

Related papers

• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Towards a Transportable Causal Network Model Based on Observational Healthcare Data [1.333879175460266]
  We propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability.
  arXiv  Detail & Related papers  (2023-11-13T13:23:31Z)
• 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)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Diagnosis Uncertain Models For Medical Risk Prediction [80.07192791931533]
  We consider a patient risk model which has access to vital signs, lab values, and prior history but does not have access to a patient's diagnosis. We show that such all-cause' risk models have good generalization across diagnoses but have a predictable failure mode. We propose a fix for this problem by explicitly modeling the uncertainty in risk prediction coming from uncertainty in patient diagnoses.
  arXiv  Detail & Related papers  (2023-06-29T23:36:04Z)
• Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation [2.202746751854349]
  We developed prognostic survival models for predicting adverse outcomes after catheter ablation treatment for AF. Traditional and deep survival models were trained to predict major bleeding events and a composite of heart failure, stroke, cardiac arrest, and death.
  arXiv  Detail & Related papers  (2022-11-22T02:55:51Z)
• Risk factor identification for incident heart failure using neural network distillation and variable selection [24.366241122862473]
  We propose two methods to untangle hidden patterns learned by an established deep learning model for risk association identification. A cohort with 788,880 (8.3% incident heart failure) patients was considered for the study. Model distillation identified 598 and 379 diseases that were associated and dissociated with heart failure at the population level, respectively. In addition to these important population-level insights, we developed an approach to individual-level interpretation to take account of varying manifestation of heart failure in clinical practice.
  arXiv  Detail & Related papers  (2021-02-17T10:20:38Z)
• 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)
• A scalable approach for developing clinical risk prediction applications in different hospitals [2.3837093461599634]
  Machine learning algorithms are now widely used in predicting acute events for clinical applications. We provide a scalable solution to extend the process of clinical risk prediction model development to multiple diseases.
  arXiv  Detail & Related papers  (2021-01-21T21:22:32Z)
• 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)
• Machine Intelligence for Outcome Predictions of Trauma Patients During Emergency Department Care [0.0]
  Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities. We hypothesized that a transfer learning based machine learning algorithm could accurately identify individuals at high risk for mortality without relying on restrictive regression model criteria. Our model achieved similar performance in age-specific comparison models and generalized well when applied to all ages simultaneously.
  arXiv  Detail & Related papers  (2020-09-08T17:26:34Z)

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.