AIMC Topic: Risk Factors

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Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

Journal of the American Heart Association
BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predi...

Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

ESC heart failure
Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (tra...

Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.

British journal of anaesthesia
BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that ...

Exploring the intersection of obesity and gender in COVID-19 outcomes in hospitalized Mexican patients: a comparative analysis of risk profiles using unsupervised machine learning.

Frontiers in public health
INTRODUCTION: Obesity and gender play a critical role in shaping the outcomes of COVID-19 disease. These two factors have a dynamic relationship with each other, as well as other risk factors, which hinders interpretation of how they influence severi...

A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
BACKGROUND: To develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms.

Machine learning for predicting Chagas disease infection in rural areas of Brazil.

PLoS neglected tropical diseases
INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening comp...

Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya.

Diabetes, obesity & metabolism
AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance d...

The relationship between heavy metals and metabolic syndrome using machine learning.

Frontiers in public health
BACKGROUND: Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets ...