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

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 ...

Risk factor identification and prediction models for ... - Altmetric

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

Risk factors and machine learning model for predicting ...

This study systematically integrated machine learning and big data to identify risk factors for hospitalization outcomes in geriatric patients ...

Cardiovascular Risk Prediction | Circulation - AHA Journals

A common criticism of risk prediction models is that they provide risk estimates for populations, not individuals. However, this naïve criticism ...

Building Risk Prediction Models for Type 2 Diabetes Using Machine ...

Of the 8 predictive models, the neural network model gave the best model performance with the highest AUC value; however, the decision tree ...

Clarifying questions about “risk factors”: predictors versus explanation

Risk stratification, prediction models or 'weather forecasting' models identify people or groups at high or elevated risk of a particular health ...

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 · 總覽 ...

Prediction of Length of Stay After Colorectal Surgery Using

This prospective multicenter study shows that a predictive model using both pre- and intraoperative risk factors can more accurately predict PLOS after ...

Opportunities and challenges in developing risk prediction models ...

Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a ...

Hospital Admission Risk Prediction [HARP]

... model that provided a longer-term assessment of risk of ... The HARP predictive models can assist health providers and planners identify patients at-risk of ...

Machine learning applications for the prediction of extended length ...

This study aimed to develop ML models for predicting eLOS in geriatric patients with hip fractures and to identify associated risk factors.

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 ...

Machine learning-enabled prediction of prolonged length of stay in ...

Therefore, identifying risk factors associated with extended PLOS is necessary. In this research, we intended to develop an interpretable ...

Factors influencing clinician and patient interaction with machine ...

Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients ...

Risk Factors Associated With Post−COVID-19 Condition

This systematic review and meta-analysis evaluates the findings of 41 studies with a total of 860 783 patients to identify risk factors ...

LASSO-Based Identification of Risk Factors and Development of a ...

The selected variables were then used to develop a model for predicting ICU mortality. AUCs of ROCs were applied to assess the prediction model, and the ...

Interpretable Predictive Models to Understand Risk Factors for ...

We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk ...

A systematic review of the prediction of hospital length of stay - PLOS

Swift identification of patients at higher risk of prolonged LoS or death will serve to significantly reduce these unavoidable costs, improve patient care ...

Preoperative Prediction and Risk Factor Identification of Hospital ...

Our machine learning models support a better understanding of the patient factors associated with different hospital LOS categories for TJA, demonstrating the ...

Clinical characteristics of Coronavirus Disease 2019 and ...

We aimed to describe the clinical characteristics of COVID-19 outside of Wuhan city; and to develop a multivariate model to predict the risk of prolonged length ...