AIMC Topic: Risk Factors

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Predictors of Stroke Outcome Extracted from Multivariate Linear Discriminant Analysis or Neural Network Analysis.

Journal of atherosclerosis and thrombosis
AIM: The prediction of functional outcome is essential in the management of acute ischemic stroke patients. We aimed to explore the various prognostic factors with multivariate linear discriminant analysis or neural network analysis and evaluate the ...

Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.

Scientific reports
Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortali...

Nonparametric variable importance assessment using machine learning techniques.

Biometrics
In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practition...

Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches.

PloS one
Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential fo...

A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study.

IEEE journal of biomedical and health informatics
Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We ret...

Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.

American journal of surgery
BACKGROUND: Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction.

Predicting risk of dyslexia with an online gamified test.

PloS one
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified ...

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BMC cancer
BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). The...

Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

Parkinsonism & related disorders
BACKGROUND: Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable h...

Identification of prognostic factors for pediatric myocarditis with a random forests algorithm-assisted approach.

Pediatric research
BACKGROUND: Pediatric myocarditis is a rare disease with substantial mortality. Little is known regarding its prognostic factors. We hypothesize that certain comorbidities and procedural needs may increase risks of poor outcomes. This study aims to i...