AIMC Topic: Logistic Models

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Gender classification in classical fiction: A computational analysis of 1113 fictions.

Mathematical biosciences and engineering : MBE
Recent decades have witnessed the rapid development of literary studies on gender and writing style. One of the common limitations of previous studies is that they analyze only a few texts, which some researchers have already pointed out. In this stu...

A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program.

Diabetes care
OBJECTIVE: Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care in Japan. We aimed to develop a machine-learning model for predicting people's failure to attend a ...

Using Explainable Artificial Intelligence Models (ML) to Predict Suspected Diagnoses as Clinical Decision Support.

Studies in health technology and informatics
The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional sup...

Potential for Machine Learning in Burn Care.

Journal of burn care & research : official publication of the American Burn Association
Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the fi...

Identification of colorectal cancer using structured and free text clinical data.

Health informatics journal
Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even with the recent screening guidelines recommending that screening begins at age 45, nearly half of all e...

Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

Health informatics journal
BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.

Shock (Augusta, Ga.)
Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Eme...

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV-A Practical Approach to Binary Classification Problems.

Acta neurochirurgica. Supplement
We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for example, the occurrence of a complication, in the statistical programming language R. To illustrate...

[Development and evaluation of a machine learning prediction model for large for gestational age].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
To develop and validate a useful predictive model for large gestational age (LGA) in pregnancy using a machine learning (ML) algorithm and compare its performance with the traditional logistic regression model. Data were obtained from the National ...