AIMC Topic: Predictive Learning Models

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Development and validation of a predictive model for depression in patients with advanced stage of cardiovascular-kidney-metabolic syndrome.

Journal of affective disorders Apr 23, 2025
Depression is highly prevalent among patients with chronic disease advanced and with poor clinical outcomes. However, effective tools for identifying individuals at risk remain limited. This study aimed to develop and validate a predictive model for ...
Depression Logistic Models Nutrition Surveys Metabolic Syndrome Predictive Learning Models Cardio-Renal Syndrome Humans Female Aged Male Middle Aged
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Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.

BMC infectious diseases Apr 22, 2025
INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. Previous studies have demonstrated that some inflammatory factors or nutritional elements contribute...
Humans Machine Learning Retrospective Studies Serum Albumin, Human Sepsis Predictive Learning Models Female Cohort Studies Risk Factors Monocytes Aged Neutrophils Male Blood Cell Count Middle Aged
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An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

Scientific reports Apr 16, 2025
The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of el...
Databases, Pharmaceutical Biological Clocks Electrodiagnosis Digestive System Diseases Predictive Learning Models Drug-Related Side Effects and Adverse Reactions Gastrointestinal Tract Constipation Nausea Vomiting Humans Reproducibility of Results Deep Learning Big Data Diarrhea ROC Curve Area Under Curve Feasibility Studies
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Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population.

Computers in biology and medicine Mar 5, 2025
OBJECTIVE: Suicide risk assessment has historically relied heavily on clinical evaluations and patient self-reports. Natural language processing (NLP) of electronic health records (EHRs) provides an alternative approach for extracting risk predictors...
Risk Assessment Case-Control Studies Adolescent Suicide Prevention United States Aged, 80 and over Young Adult Adult Male Middle Aged Predictive Learning Models Psychological Well-Being Natural Language Processing United States Department of Veterans Affairs Veterans United States Department of Defense Suicide, Completed Humans Electronic Health Records Female Aged
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Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.

PloS one Jan 30, 2025
A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researche...
Health Expenditures Quality Improvement Remote Consultation Patient Satisfaction Large Language Models Predictive Learning Models Republic of Korea Emergency Room Visits Humans Surveys and Questionnaires Triage Natural Language Processing Hospitals, University
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Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

Annals of surgical oncology Jan 28, 2025
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Humans Random Forest Machine Learning Middle Aged Carcinoma, Neuroendocrine Female Retrospective Studies Risk Assessment SEER Program Aged Nomograms Proportional Hazards Models Stomach Neoplasms United States Predictive Learning Models Survival Rate Regression Analysis Male
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.

International dental journal Jan 5, 2025
OBJECTIVE: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.
Incisor Body Size Maxilla Skull Base Adolescent Development Predictive Learning Models Radiography Cephalometry Random Forest Face Random Allocation Humans Child Retrospective Studies Mandible Reproducibility of Results Puberty Male Regression Analysis Adolescent Predictive Value of Tests
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StUdy of Gestational diabetes And Risk using Electronic Data (SUGARED): a population-based cohort study-study protocol.

BMJ open Dec 26, 2024
INTRODUCTION: The incidence of gestational diabetes mellitus (GDM) in Australia has tripled in the last 20 years. Consequently, over 40 000 pregnancies are now diagnosed as 'higher risk' each year. This has increased antenatal surveillance and obstet...
Research Design New South Wales Hospitals, Public Retrospective Studies Routinely Collected Health Data Pregnancy Glucose Tolerance Test Predictive Learning Models Female Information Storage and Retrieval Risk Assessment Propensity Score Incidence Humans Delivery, Obstetric Pregnancy Outcome Multilevel Analysis Logistic Models Observational Studies as Topic Diabetes, Gestational
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Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation.

Frontiers in endocrinology Oct 8, 2024
INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) represents a major global health challenge, often undiagnosed because of suboptimal screening tools. Advances in machine learning (ML) offer potential improvements in predictive diagnostics, lev...
Middle Aged Humans Nutrition Surveys Prognosis Non-alcoholic Fatty Liver Disease Female Predictive Learning Models Adult Male
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Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.

Scientific reports Sep 3, 2024
Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their stan...
Feasibility Studies DNA Repair Predictive Learning Models Chromosomal Instability Machine Learning Female Humans Reproducibility of Results Cell Proliferation Cell Line, Tumor DNA Copy Number Variations Triple Negative Breast Neoplasms Area Under Curve Neoplastic Cells, Circulating Whole Genome Sequencing
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