Risk factor identification and prediction models for prolonged length ...

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks

The artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic ...

Risk factor identification and prediction models for prolonged length ...

This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time ...

Risk factor identification and prediction models for prolonged length ...

PDF | Background Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on ...

Machine Learning Model for Predicting Risk Factors of Prolonged ...

Given the screened variables and prediction models, the XGBoost model demonstrated superior predictive performance in identifying prolonged LOS, ...

Preoperative Prediction and Risk Factor Identification of Hospital ...

The random forest models suggested that a lower Risk Assessment and Prediction Tool score, unplanned admission or hospital transfer, and a medical history of ...

Identifying Risk Factors for Prolonged Length of Stay in Hospital and ...

A logistic regression model based on parameters at discharge achieved an area under the curve of 0.840 to 0.896 for prolonged LOS prediction, ...

Identification of risk factors of Long COVID and predictive modeling ...

For the survival analysis setting, we used a multivariate Cox proportional hazard model with L2 norm regularization to predict the time to the ...

Oncology & Machine Learning on X: "Risk factor identification and ...

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial ...

Development and Internal Validation of a Prediction Model for Falls ...

Calibration plots demonstrate the relation between the predicted and observed fall risk. The diagonal line represents perfect calibration. The ...

Machine learning-based prediction of hospital prolonged length of ...

We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and ...

Towards Predicting Length of Stay and Identification of Cohort Risk ...

The purpose of this study was to forecast patients' LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS.

A predictive model for the early identification of patients at risk for a ...

Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study ...

Hospital length of stay prediction for general surgery and total knee ...

We showed that these risk prediction models were infrequently externally validated with poor study quality, typically related to poor reporting.

Addressing the implementation challenge of risk prediction model ...

However, this approach works only when the preconditioning outcome and the subset of risk factors from the original data used to develop the ...

Statistical Primer: developing and validating a risk prediction model

Abstract. A risk prediction model is a mathematical equation that uses patient risk factor data to estimate the probability of a patient ...

Towards interpretable, medically grounded, EMR-based risk ...

Machine-learning based risk prediction models have the potential to improve patient outcomes by assessing risk more accurately than ...

Identification of Risk Factors and Machine Learning-Based ... - MDPI

The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction ...

Prediction models for cardiovascular disease risk in the general ...

The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used ...

Why predicting risk can't identify 'risk factors': empirical assessment ...

Stability Algorithms. It is known some algorithms that reduce the size of the model, such as LASSO regression, are not consistent variable selectors and ...