AIMC Topic: Prognosis

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Big data in IBD: big progress for clinical practice.

Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation se...

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.

Scientific reports
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel netw...

The application of unsupervised deep learning in predictive models using electronic health records.

BMC medical research methodology
BACKGROUND: The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder fea...

Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study.

Circulation. Heart failure
BACKGROUND: Implantable cardiac sensors have shown promise in reducing rehospitalization for heart failure (HF), but the efficacy of noninvasive approaches has not been determined. The objective of this study was to determine the accuracy of noninvas...

Exercise cardiac power and the risk of heart failure in men: A population-based follow-up study.

Journal of sport and health science
BACKGROUND: Little is known about exercise cardiac power (ECP), defined as the ratio of directly measured maximal oxygen uptake with peak systolic blood pressure during exercise, on heart failure (HF) risk. We examined the association of ECP and the ...

Prediction of perioperative transfusions using an artificial neural network.

PloS one
BACKGROUND: Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict...

Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.

BioMed research international
We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification sys...

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.

Circulation. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenua...