AIMC Topic: Machine Learning

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Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning.

PloS one
This study explores the challenge of differentiating autism spectrum (AS) from non-AS conditions in adolescents and adults, particularly considering the heterogeneity of AS and the limitations ofssss diagnostic tools like the ADOS-2. In response, we ...

Machine learning of the prime distribution.

PloS one
In the present work we use maximum entropy methods to derive several theorems in probabilistic number theory, including a version of the Hardy-Ramanujan Theorem. We also provide a theoretical argument explaining the experimental observations of Y.-H....

Factors predicting access to medications for opioid use disorder for housed and unhoused patients: A machine learning approach.

PloS one
BACKGROUND: Opioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in the United States in recent years. While medications for opioid use disorder (MOUD) effectively redu...

Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.

Interdisciplinary sciences, computational life sciences
Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to...

Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy
INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to ...

Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: The aim of this study was to develop and train a machine learning (ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate recurrence rates following an ar...

Prediction of primary admission total charges following cervical disc arthroplasty utilizing machine learning.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Cervical disc arthroplasty (CDA) has become an increasingly popular alternative to anterior cervical discectomy and fusion, offering benefits such as motion preservation and reduced risk of adjacent segment disease. Despite its ad...

Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.

Journal of dental education
PURPOSE AND OBJECTIVE: Objective, valid, and reliable evaluations are needed in order to develop haptic skills in dental education. The aim of this study is to investigate the validity and reliability of the machine learning method in evaluating the ...

Are automated video interviews smart enough? Behavioral modes, reliability, validity, and bias of machine learning cognitive ability assessments.

The Journal of applied psychology
Automated video interviews (AVIs) that use machine learning (ML) algorithms to assess interviewees are increasingly popular. Extending prior AVI research focusing on noncognitive constructs, the present study critically evaluates the possibility of a...

Relationship matters: Using machine learning methods to predict the mental health severity of Chinese college freshmen during the pandemic period.

Journal of affective disorders
BACKGROUND: Pandemics act as stressors and may lead to frequent mental health disorders. College student, especially freshmen, are particularly susceptible to experiencing intense mental stress reactions during a pandemic. We aimed to identify stable...