Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health

• URL: http://arxiv.org/abs/2406.14953v2
• Date: Tue, 2 Jul 2024 11:22:36 GMT
• Title: Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health
• Authors: Guangkun Nie, Qinghao Zhao, Gongzheng Tang, Jun Li, Shenda Hong,
• Abstract summary: Photoplethysmography is emerging as a crucial tool for monitoring human hemodynamics. Real-world age distributions are often imbalanced, posing significant challenges for deep learning models. We introduce a novel, simple, and effective loss function named the Dist Loss to address deep imbalanced regression tasks.
• Score: 13.534687795079892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning. However, real-world age distributions are often imbalanced, posing significant challenges for deep learning models. In this paper, we introduce a novel, simple, and effective loss function named the Dist Loss to address deep imbalanced regression tasks. We trained a one-dimensional convolutional neural network (Net1D) incorporating the Dist Loss on the extensive UK Biobank dataset (n=502,389) to estimate vascular age from PPG signals and validate its efficacy in characterizing cardiovascular health. The model's performance was validated on a 40% held-out test set, achieving state-of-the-art results, especially in regions with small sample sizes. Furthermore, we divided the population into three subgroups based on the difference between predicted vascular age and chronological age: less than -10 years, between -10 and 10 years, and greater than 10 years. We analyzed the relationship between predicted vascular age and several cardiovascular events over a follow-up period of up to 10 years, including death, coronary heart disease, and heart failure. Our results indicate that the predicted vascular age has significant potential to reflect an individual's cardiovascular health status. Our code will be available at https://github.com/Ngk03/AI-vascular-age.

Related papers

• Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data [4.285399998352862]
  We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA) which are explainable and easily interpretable.
  arXiv  Detail & Related papers  (2024-07-23T11:05:20Z)
• 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)
• Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals [0.8108972030676012]
  We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages. Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.
  arXiv  Detail & Related papers  (2023-10-11T13:05:28Z)
• Simulation-based Inference for Cardiovascular Models [57.92535897767929]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters. We perform an in-silico uncertainty analysis of five biomarkers of clinical interest. We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP) [2.649401887836554]
  Acute Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly interrupted because of a blocked artery. Current standard AIS assessment method, which thresholds the 3D measurement maps extracted from Computed Tomography Perfusion (CTP) images, is not accurate enough. We propose imbalanced Temporal Deep Process (iTDGP), a probabilistic model that can improve AIS prediction by using baseline Gaussian time series.
  arXiv  Detail & Related papers  (2022-11-16T17:32:29Z)
• Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System [0.40151799356083057]
  The prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. We present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. We achieve a final prediction accuracy of 69 % in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and 50 +- 10.7 % F1-score.
  arXiv  Detail & Related papers  (2022-04-25T21:20:27Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation [18.503467872057424]
  The concept of biological age (BA) is hard to grasp mainly due to the lack of a clearly defined reference standard. We propose a new imaging-based framework for organ-specific BA estimation.
  arXiv  Detail & Related papers  (2020-09-22T19:04:02Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

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.