AIMC Topic: Logistic Models

Clear Filters Showing 471 to 480 of 1261 articles

Dementia risk predictions from German claims data using methods of machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regression, and what the important predictors for dementia risk are.

Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algor...

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

Journal of biomedical informatics
BACKGROUND: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful ...

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when devel...

Epidemiological predictive modeling: lessons learned from the Kuopio ischemic heart disease risk factor study.

Annals of epidemiology
PURPOSE: The use of predictive models in epidemiology is relatively narrow as most of the studies report results of traditional statistical models such as Linear, Logistic, or Cox regressions. In this study, a high-dimensional epidemiological cohort,...

Application of machine learning based methods in exposure-response analysis.

Journal of pharmacokinetics and pharmacodynamics
Robust estimation of exposure response analysis relies on correct specification of the model structure with traditional parametric approach. However, the assumptions of the handcrafted model may not always hold or verifiable. Here, we conducted a sim...

Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients.

The Journal of surgical research
INTRODUCTION: Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have ...

Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System.

Computational intelligence and neuroscience
Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intel...

Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

International journal of laboratory hematology
INTRODUCTION: Wrong blood in tube (WBIT) errors are a significant patient-safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood...

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

World neurosurgery
OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of ...