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Logistic Models

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Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms.

Archives of gynecology and obstetrics
PURPOSE: Short- and long-term complications of gestational diabetes mellitus (GDM) involving pregnancies and offspring warrant the development of an effective individualized risk prediction model to reduce and prevent GDM together with its associated...

Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.

Journal of cancer research and clinical oncology
PURPOSE: Due to the rarity of primary gastrointestinal lymphoma (PGIL), the prognostic factors and optimal management of PGIL have not been clearly defined. We aimed to establish prognostic models using a deep learning algorithm for survival predicti...

SCOPE: predicting future diagnoses in office visits using electronic health records.

Scientific reports
We propose an interpretable and scalable model to predict likely diagnoses at an encounter based on past diagnoses and lab results. This model is intended to aid physicians in their interaction with the electronic health records (EHR). To accomplish ...

A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients.

Surgery
BACKGROUND: Acute kidney injury is a common postoperative complication affecting between 10% and 30% of surgical patients. Acute kidney injury is associated with increased resource usage and chronic kidney disease development, with more severe acute ...

Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery.

Surgical endoscopy
BACKGROUND: Postoperative gastrointestinal bleeding (GIB) is a rare but serious complication of bariatric surgery. The recent rise in extended venous thromboembolism regimens as well as outpatient bariatric surgery may increase the risk of postoperat...

A forensic evaluation method for DeepFake detection using DCNN-based facial similarity scores.

Forensic science international
Detecting DeepFake videos has become a central task in modern multimedia forensics applications. This article presents a method to detect face swapped videos when the portrayed person in the video is known. We propose using a threshold classifier bas...

The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery.

ANZ journal of surgery
BACKGROUND: This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery.

Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified.

PloS one
Differentiating the intrinsic subtypes of breast cancer is crucial for deciding the best treatment strategy. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods, but to date, deep lear...

A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population.

International orthodontics
INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample.