Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

• URL: http://arxiv.org/abs/2403.01533v1
• Date: Sun, 3 Mar 2024 15:23:49 GMT
• Title: Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
• Authors: Bijan Roudini, Boshra Khajehpiri, Hamid Abrishami Moghaddam, and Mohamad Forouzanfar
• Abstract summary: We investigated the performance of tree-based ML algorithms to predict all-cause mortality within 14 years. Adding bPEP and bET to our feature set significantly improved the algorithms' performance. This advancement may enable better treatment prioritization for high-risk individuals.
• Score: 0.35998666903987897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise estimation of cardiac patients' current and future comorbidities is an important factor in prioritizing continuous physiological monitoring and new therapies. ML models have shown satisfactory performance in short-term mortality prediction of patients with heart disease, while their utility in long-term predictions is limited. This study aims to investigate the performance of tree-based ML models on long-term mortality prediction and the effect of two recently introduced biomarkers on long-term mortality. This study utilized publicly available data from CCHIA at the Ministry of Health and Welfare, Taiwan, China. Medical records were used to gather demographic and clinical data, including age, gender, BMI, percutaneous coronary intervention (PCI) status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction (STEMI), and non-STEMI. Using medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), collected from 139 patients with acute myocardial infarction, we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years. The developed ML models achieved significantly better performance compared to the baseline LR (C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost, vs 0.77 for LR) (P-RF<0.001, PAdaBoost<0.001, PXGBoost<0.05). Adding bPEP and bET to our feature set significantly improved the algorithms' performance, leading to an absolute increase in C-Statistic of up to 0.03 (C-Statistic, 0.83 for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs 0.74 for LR) (P-RF<0.001, PAdaBoost<0.001, PXGBoost<0.05). This advancement may enable better treatment prioritization for high-risk individuals.

Related papers

• Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning [3.4335475695580127]
  Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality.<n>We developed a machine learning framework to predict 30-day in-hospital mortality among ICU patients with HKD.
  arXiv  Detail & Related papers  (2025-07-25T00:48:23Z)
• Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV [49.1574468325115]
  This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset.<n>The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer)
  arXiv  Detail & Related papers  (2025-05-23T14:06:42Z)
• A Transformer-based survival model for prediction of all-cause mortality in heart failure patients: a multi-cohort study [5.831730826863567]
  We developed and validated TRisk, a Transformer-based AI model predicting 36-month mortality in heart failure patients. Our study included 403,534 heart failure patients (ages 40-90) from 1,418 English general practices.
  arXiv  Detail & Related papers  (2025-03-16T01:53:50Z)
• XGBoost-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database [0.0]
  Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models to predict Intensive Care Unit (ICU) mortality in SA-AKI patients.
  arXiv  Detail & Related papers  (2025-02-25T08:49:22Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring. Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
  We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE) CRTRE applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
  arXiv  Detail & Related papers  (2024-11-10T02:40:06Z)
• Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
  Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
  arXiv  Detail & Related papers  (2024-10-18T12:37:51Z)
• Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database [1.5186937600119894]
  Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes.
  arXiv  Detail & Related papers  (2024-09-03T07:57:08Z)
• New spectral imaging biomarkers for sepsis and mortality in intensive care [0.23592579902069336]
  The driving hypothesis of this study was that hyperspectral imaging (HSI) could provide novel biomarkers for sepsis diagnosis and treatment management. We conducted a comprehensive study involving HSI data of the palm and fingers from more than 480 patients on the day of their intensive care unit admission.
  arXiv  Detail & Related papers  (2024-08-19T10:24:57Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• 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)
• Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction [0.0]
  Cardiovascular disease remains a leading cause of mortality in the contemporary world. Accurate predictions are pivotal for refining healthcare strategies. XGBoost emerges as the top-performing model.
  arXiv  Detail & Related papers  (2023-11-01T13:41:44Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation [22.45448597986172]
  Multilabel gradient boosting machine provided the best discriminant power for stroke, major bleeding, and death. Models identified additional risk features (such as hemoglobin level, renal function, etc.) for each outcome.
  arXiv  Detail & Related papers  (2022-02-02T15:15:03Z)
• An early warning tool for predicting mortality risk of COVID-19 patients using machine learning [0.0]
  A retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. A nomogram was developed for predicting the mortality risk among COVID-19 patients. An integrated score (LNLCA) was calculated with the corresponding death probability.
  arXiv  Detail & Related papers  (2020-07-29T15:16:09Z)
• 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)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

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.