AIMC Topic: Logistic Models

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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...

Application Value of the Automated Machine Learning Model Based on Modified Computed Tomography Severity Index Combined With Serological Indicators in the Early Prediction of Severe Acute Pancreatitis.

Journal of clinical gastroenterology
BACKGROUND AND AIMS: Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. To assess the value of the Modified Computed Tomography Severity Index (MCTSI) combined w...

Predictive modeling and socioeconomic determinants of diarrhea in children under five in the Amhara Region, Ethiopia.

Frontiers in public health
BACKGROUND: Diarrheal disease, characterized by high morbidity and mortality rates, continues to be a serious public health concern, especially in developing nations such as Ethiopia. The significant burden it imposes on these countries underscores t...

Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), defined as greater than 50 days, for patients with oropharyngeal squamous cell carcinoma (OPSCC).

Predicting recurrent gestational diabetes mellitus using artificial intelligence models: a retrospective cohort study.

Archives of gynecology and obstetrics
BACKGROUND: We aimed to develop novel artificial intelligence (AI) models based on early pregnancy features to forecast the likelihood of recurrent gestational diabetes mellitus (GDM) before 14 weeks of gestation in subsequent pregnancies.

Exploring Machine Learning Algorithms to Predict Diarrhea Disease and Identify its Determinants among Under-Five Years Children in East Africa.

Journal of epidemiology and global health
BACKGROUND: The second most common cause of death for children under five is diarrhea. Early Predicting diarrhea disease and identify its determinants (factors) using an advanced machine learning model is the most effective way to save the lives of c...