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Predictive Learning Models

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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 39439566
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...
Adult Female Humans Male Middle Aged Non-alcoholic Fatty Liver Disease Nutrition Surveys Predictive Learning Models Prognosis
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StUdy of Gestational diabetes And Risk using Electronic Data (SUGARED): a population-based cohort study-study protocol.

BMJ open 39725433
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...
Delivery, Obstetric Diabetes, Gestational Female Glucose Tolerance Test Hospitals, Public Humans Incidence Information Storage and Retrieval Logistic Models Multilevel Analysis New South Wales Observational Studies as Topic Predictive Learning Models Pregnancy Pregnancy Outcome Propensity Score Research Design Retrospective Studies Risk Assessment Routinely Collected Health Data
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Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.

PloS one 39883641
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...
Emergency Room Visits Health Expenditures Hospitals, University Humans Large Language Models Natural Language Processing Patient Satisfaction Predictive Learning Models Quality Improvement Remote Consultation Republic of Korea Surveys and Questionnaires Triage
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Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

Annals of surgical oncology 39873848
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).
Aged Carcinoma, Neuroendocrine Female Humans Machine Learning Male Middle Aged Nomograms Predictive Learning Models Proportional Hazards Models Random Forest Regression Analysis Retrospective Studies Risk Assessment SEER Program Stomach Neoplasms Survival Rate United States
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.

International dental journal 39757033
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.
Adolescent Adolescent Development Body Size Cephalometry Child Face Humans Incisor Male Mandible Maxilla Predictive Learning Models Predictive Value of Tests Puberty Radiography Random Allocation Random Forest Regression Analysis Reproducibility of Results Retrospective Studies Skull Base
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Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.

Journal of cosmetic dermatology 39918034
OBJECTIVES: To establish, evaluate and compare three recurrence prediction models for keloid patients using machine learning methods.
Adolescent Adult Decision Trees Female Humans Keloid Machine Learning Male Middle Aged Postoperative Complications Predictive Learning Models Prognosis Recurrence ROC Curve
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Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

Computer methods and programs in biomedicine 40188576
BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary information. However, missing modalities are common in clinical settings, limiting the effectiveness of...
Area Under Curve Classification Algorithms Data Accuracy Female Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Liver Neoplasms Male Multimodal Imaging Predictive Learning Models Ultrasonography
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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 40048900
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...
Adolescent Adult Aged Aged, 80 and over Case-Control Studies Electronic Health Records Female Humans Male Middle Aged Natural Language Processing Predictive Learning Models Psychological Well-Being Risk Assessment Suicide Prevention Suicide, Completed United States United States Department of Defense United States Department of Veterans Affairs Veterans Young Adult
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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 40264028
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...
Aged Blood Cell Count Cohort Studies Female Humans Machine Learning Male Middle Aged Monocytes Neutrophils Predictive Learning Models Retrospective Studies Risk Factors Sepsis Serum Albumin, Human
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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 40240387
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...
Area Under Curve Big Data Biological Clocks Constipation Databases, Pharmaceutical Deep Learning Diarrhea Digestive System Diseases Drug-Related Side Effects and Adverse Reactions Electrodiagnosis Feasibility Studies Gastrointestinal Tract Humans Nausea Predictive Learning Models Reproducibility of Results ROC Curve Vomiting
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