AIMC Topic: Logistic Models

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Predicting the risk of asthma development in youth using machine learning models.

PloS one
Asthma is a chronic respiratory disease characterized by wheezing and difficulty breathing, which disproportionally affects 4.7 million children in the U.S. Currently, there is a lack of asthma predictive models for youth with good performance. This ...

Integrating machine learning and time-to-event models to explain and predict risk of hospitalization due to dengue in Colombia.

Scientific reports
Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the...

Mortality risk prediction in NSTE-ACS following PCI: Insights from a real-world cohort.

PloS one
BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a major contributor to cardiovascular mortality, yet reliable tools for individualized mortality prediction remain limited. Machine learning offers the potential to enhance pr...

Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.

Annals of medicine
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...

Development of a machine learning-based interface for insulin dependency prediction using clinical data.

Scientific reports
Diabetes mellitus is a major global health burden, and early identification of insulin dependency is important for timely intervention. This study developed an artificial intelligence-based diagnostic system using a real-world clinical dataset of 100...

Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.

Scientific reports
We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the ...

Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality.

Journal of medical Internet research
The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over traditional statistical logistic regression. Althou...

Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...

Machine learning models for risk prediction of age-related macular degeneration in Fujian eye study.

PloS one
OBJECTIVE: Age-related macular degeneration (AMD) is a retinal disorder that significantly impairs vision. This study investigates various machine learning models for predicting AMD risk, laying the groundwork for further research using big data and ...

Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW), defined as a newborn weighing less than 2500 g, is an increasingly significant public health concern. Exploring the risk and protective factors for LBW is getting more and more important. This study aimed to utiliz...