AIMC Topic: Middle Aged

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Prediction of 24-Hour Urinary Sodium Excretion Using Machine-Learning Algorithms.

Journal of the American Heart Association
BACKGROUND: Accurate quantification of sodium intake based on self-reported dietary assessments has been a persistent challenge. We aimed to apply machine-learning (ML) algorithms to predict 24-hour urinary sodium excretion from self-reported questio...

Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning.

Journal of the American Heart Association
BACKGROUND: Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug-induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and ...

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

Respiratory research
BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

Critical care (London, England)
BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous ...

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

BMC surgery
PURPOSE: The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoper...

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpret...

Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports.

JMIR public health and surveillance
BACKGROUND: Smell disorders are commonly reported with COVID-19 infection. The smell-related issues associated with COVID-19 may be prolonged, even after the respiratory symptoms are resolved. These smell dysfunctions can range from anosmia (complete...

Machine learning-based bioimpedance assessment of knee osteoarthritis severity.

Biomedical physics & engineering express
This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern...

Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.

Frontiers in endocrinology
PURPOSE: The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here,...