MIEO: encoding clinical data to enhance cardiovascular event prediction

• URL: http://arxiv.org/abs/2510.11257v1
• Date: Mon, 13 Oct 2025 10:47:49 GMT
• Title: MIEO: encoding clinical data to enhance cardiovascular event prediction
• Authors: Davide Borghini, Davide Marchi, Angelo Nardone, Giordano Scerra, Silvia Giulia Galfrè, Alessandro Pingitore, Giuseppe Prencipe, Corrado Priami, Alina Sîrbu,
• Abstract summary: Machine learning methods have been employed to extract knowledge from clinical data and predict clinical events.<n>While promising, approaches suffer from at least two main issues: low availability of labelled data and data leading to missing values.<n>This work proposes the use of self-supervised auto-encoders to efficiently address these challenges.
• Score: 31.458406135473805
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, approaches suffer from at least two main issues: low availability of labelled data and data heterogeneity leading to missing values. This work proposes the use of self-supervised auto-encoders to efficiently address these challenges. We apply our methodology to a clinical dataset from patients with ischaemic heart disease. Patient data is embedded in a latent space, built using unlabelled data, which is then used to train a neural network classifier to predict cardiovascular death. Results show improved balanced accuracy compared to applying the classifier directly to the raw data, demonstrating that this solution is promising, especially in conditions where availability of unlabelled data could increase.

Related papers

• Predicting Clinical Outcomes with Waveform LSTMs [0.0]
  This study evaluates the potential of leveraging clinical waveform data to improve prediction accuracy on a single benchmark task.<n>We identify significant potential from this data, beating the existing baselines for both logistic regression and deep learning models.
  arXiv  Detail & Related papers  (2025-03-13T22:19:05Z)
• FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data [52.55123685248105]
  Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. This paper presents the first real-world FL benchmark for cardiovascular disease detection, named FedCVD.
  arXiv  Detail & Related papers  (2024-10-28T02:24:01Z)
• Multimodal Pretraining of Medical Time Series and Notes [45.89025874396911]
  Deep learning models show promise in extracting meaningful patterns, but they require extensive labeled data. We propose a novel approach employing self-supervised pretraining, focusing on the alignment of clinical measurements and notes. In downstream tasks, including in-hospital mortality prediction and phenotyping, our model outperforms baselines in settings where only a fraction of the data is labeled.
  arXiv  Detail & Related papers  (2023-12-11T21:53:40Z)
• Time-dependent Iterative Imputation for Multivariate Longitudinal Clinical Data [0.0]
  Time-Dependent Iterative imputation offers a practical solution for imputing time-series data. When applied to a cohort consisting of more than 500,000 patient observations, our approach outperformed state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2023-04-16T16:10:49Z)
• Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction [102.23901690661916]
  Low-dimensional embedding spaces can be derived from the UK Biobank population dataset to enhance data-scarce prediction of health indicators, lifestyle and demographic characteristics. Performances gains from semisupervison approaches will probably become an important ingredient for various medical data science applications.
  arXiv  Detail & Related papers  (2021-10-12T16:25:50Z)
• A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica [0.5541644538483946]
  Training a deep learning model requires a considerable amount of labeled images. A number of publicly available datasets have been built with data from different hospitals and clinics. The use of the semi-supervised deep learning approach known as MixMatch, to leverage the usage of unlabeled data is proposed and evaluated.
  arXiv  Detail & Related papers  (2021-07-24T22:26:50Z)
• Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks [4.040013871160853]
  Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients. Applying machine learning models to electronic health records is a challenge due to the pervasiveness of missing values.
  arXiv  Detail & Related papers  (2021-06-22T15:49:01Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.