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Logistic Models

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Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA. MATERIAL AN...

Exploring the accuracy of tooth loss prediction between a clinical periodontal prognostic system and a machine learning prognostic model.

Journal of clinical periodontology
AIM: The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)-based model for the prediction of tooth loss in periodontitis patients under supportive period...

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

PloS one
OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithm...

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study.

International journal of medical informatics
BACKGROUND: Prediction of mortality is very important for care planning in hospitalized patients with dementia and artificial intelligence has the potential to serve as a solution; however, this issue remains unclear. Thus, this study was conducted t...

Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?

BMC medical research methodology
PURPOSE: Propensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised autoencoders for propensity...

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

The American journal of managed care
OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitali...

Random forests for the analysis of matched case-control studies.

BMC bioinformatics
BACKGROUND: Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption...

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Scientific reports
Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It...

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases.

International journal of surgery (London, England)
BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practic...