AIMC Topic: Risk Factors

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Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score.

Age and ageing
BACKGROUND: the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognised tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional informat...

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Briefings in bioinformatics
Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. How...

[Comparison of the predictive performance of Logistic regression, BP neural network and support vector machine model for the risk of acute exacerbation of readmission in elderly patients with chronic obstructive pulmonary disease within 30 days].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To compare the effectiveness of Logistic regression, BP neural network and support vector machine models in the prediction of 30-day risk of readmission in elderly patients with an exacerbation of chronic obstructive pulmonary disease (COP...

Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders.

Anesthesiology
BACKGROUND: Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery-...

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes.

Studies in health technology and informatics
Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can help determine which patients are at greater risk of hospital readmission for early intervention. Ou...

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

International journal of epidemiology
BACKGROUND: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep le...

The Prediction of Fall Circumstances Among Patients in Clinical Care - A Retrospective Observational Study.

Studies in health technology and informatics
Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We devel...

[Research progress of in-hospital mortality risk model in patients with acute myocardial infarction].

Zhonghua wei zhong bing ji jiu yi xue
The incidence of in-hospital death in acute myocardial infarction (AMI) is high, which seriously threatens the life and health of patients. At present, many countries and regions have established a variety of objective assessment models for predictin...

Fighting against sudden cardiac death: need for a paradigm shift-Adding near-term prevention and pre-emptive action to long-term prevention.

European heart journal
More than 40 years after the first implantable cardioverter-defibrillator (ICD) implantation, sudden cardiac death (SCD) still accounts for more than five million deaths worldwide every year. Huge efforts in the field notwithstanding, it is now incre...