AIMC Topic: Machine Learning

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Identifying Preliminary Risk Profiles for Dissociation in 16- to 25-Year-Olds Using Machine Learning.

Early intervention in psychiatry Feb 1, 2025
INTRODUCTION: Dissociation is associated with clinical severity, increased risk of suicide and self-harm, and disproportionately affects adolescents and young adults. Whilst evidence indicates multiple factors contribute to dissociative experiences, ...
Cross-Sectional Studies Dissociative Disorders Male Adolescent Humans Stress, Psychological Machine Learning Adverse Childhood Experiences United Kingdom Female Loneliness Risk Factors Young Adult Adult
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Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.

Journal of cosmetic dermatology Feb 1, 2025
OBJECTIVES: To establish, evaluate and compare three recurrence prediction models for keloid patients using machine learning methods.
Predictive Learning Models ROC Curve Middle Aged Keloid Prognosis Decision Trees Humans Postoperative Complications Adult Recurrence Female Adolescent Machine Learning Male
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Exploring Ensemble Learning Techniques for Infant Mortality Prediction: A Technical Analysis of XGBoost Stacking AdaBoost and Bagging Models.

Birth defects research Feb 1, 2025
BACKGROUND: Infant mortality remains a critical public health issue, reflecting the overall health and well-being of a population. Accurate prediction of infant mortality is crucial, as it enables healthcare providers to identify at-risk populations ...
Infant Mortality Ensemble Learning Boosting Machine Learning Algorithms Female Infant, Newborn Infant India Humans Machine Learning
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Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.

Journal of consulting and clinical psychology Feb 1, 2025
OBJECTIVE: This study applied a machine-learning-based skill assessment system to investigate the association between supportive counseling skills (empathy, open questions, and reflections) and treatment outcomes. We hypothesized that higher empathy ...
Female Young Adult Humans Machine Learning Empathy Clinical Competence Professional-Patient Relations Adult Treatment Outcome Male Psychotherapy Middle Aged Counseling
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Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

Pharmacology research & perspectives Feb 1, 2025
The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diabetes (T2D) in the U.S. In Japan, there is little evidence on the HbA1c-lowering effect of high-dose ...
Japan Metformin Longitudinal Studies Dose-Response Relationship, Drug East Asian People Glycated Hemoglobin Middle Aged Humans Diabetes Mellitus, Type 2 Hypoglycemic Agents Aged Male Machine Learning Female
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Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias Feb 1, 2025
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.
Risk Assessment ROC Curve Shock, Septic Regression Analysis Aged Male Middle Aged Emergency Service, Hospital Registries Norepinephrine Humans Intensive Care Units Female Machine Learning Retrospective Studies
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Qualitative analysis of terpenoid esters based on near-infrared spectroscopy and machine learning.

The Review of scientific instruments Feb 1, 2025
This study delves into the method of qualitative analysis of terpenoid esters using near-infrared spectroscopy technology. Terpenoid esters are bioactive compounds widely used in the pharmaceutical and cosmetics industries. Near-infrared spectroscopy...
Neural Networks, Computer Machine Learning Algorithms Spectroscopy, Near-Infrared Esters Terpenes
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A Generalized Machine Learning Model for Identifying Congenital Heart Defects (CHDs) Using ICD Codes.

Birth defects research Feb 1, 2025
BACKGROUND: International Classification of Diseases (ICD) codes utilized for congenital heart defect (CHD) case identification in datasets have substantial false-positive (FP) rates. Incorporating machine learning (ML) algorithms following case sele...
False Positive Reactions International Classification of Diseases Machine Learning Heart Defects, Congenital Humans Algorithms Female Male
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Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.

Physiological reports Feb 1, 2025
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requi...
Muscle, Skeletal Thigh Body Composition Resistance Training Adult Pilot Projects Magnetic Resonance Imaging Female Subcutaneous Fat Young Adult Humans Machine Learning
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Instar identification and weight prediction of (Guenée) larvae using machine learning.

Bulletin of entomological research Feb 1, 2025
The Asian corn borer, (Guenée), emerges as a significant threat to maize cultivation, inflicting substantial damage upon the crops. Particularly, its larval stage represents a critical point characterised by significant economic consequences on maiz...
Larva Body Weight Moths Insect Control Machine Learning Animals
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