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Logistic Models

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Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm.

Biometrical journal. Biometrische Zeitschrift
Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving more care are less healthy and at higher risk of poor outcomes. We used data from seven health systems ...

Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.

PloS one
Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a...

Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks.

Aging
Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate and early prediction of mortality is critical and difficult. Three prediction models, the logistic regression (LR) model, artificial neural network (AN...

Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals fr...

Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis.

International journal of medical informatics
INTRODUCTION: We aimed to assess whether machine learning models are superior at predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional prediction model.

Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

Cancer medicine
BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning m...

The Comprehensive Machine Learning Analytics for Heart Failure.

International journal of environmental research and public health
: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. H...

Application of machine learning in predicting hospital readmissions: a scoping review of the literature.

BMC medical research methodology
BACKGROUND: Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US.