AIMC Topic: Outpatients
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Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.
Scientific reports
May 23, 2025
Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypox...
Splenic and portal venous flow associated with frailty and sarcopenia in older outpatients with cardiovascular disease.
BMC geriatrics
May 9, 2025
BACKGROUND: Older patients with cardiovascular disease often experience frailty and sarcopenia. We evaluated whether a reduced blood flow in the splenic and portal vein is associated with frailty and sarcopenia in older patients with cardiovascular d...
Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital.
Archives of endocrinology and metabolism
Apr 15, 2025
OBJECTIVE: To characterize, via a predictive model using real-world data, patients with diabetes with a heightened probability of hospitalization.
Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China.
BMC public health
Apr 9, 2025
BACKGROUND: Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate info...
Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.
BMJ open quality
Mar 22, 2025
OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-lev...
Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos.
Journal of neurodevelopmental disorders
Dec 24, 2024
BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder, affecting between 5% and 7% of school-age children. ADHD is typically characterized by persistent patterns of inattention or hyperactivity-...
Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol.
Nutrients
Dec 6, 2024
BACKGROUND/OBJECTIVES: Child acute malnutrition is a global public health problem, affecting 45 million children under 5 years of age. The World Health Organization recommends monitoring weight gain weekly as an indicator of the correct treatment. Ho...
Predicting mental health treatment outcomes using latent growth mixture models and machine learning in a real-world clinical setting.
Journal of psychiatric research
Dec 4, 2024
Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population...
Machine Learning
Retrospective Studies
Female
Reproducibility of Results
Aged
Aged, 80 and over
Young Adult
Adult
Male
Area Under Curve
Humans
Outcome Assessment, Health Care
Veterans
Outpatients
Patient Health Questionnaire
Stress Disorders, Post-Traumatic
ROC Curve
Middle Aged
United States
Depression
Treatment Outcome
Latent Class Analysis
Mental Health Services
Adolescent
Predictors for improvement in personality functioning during outpatient psychotherapy: A machine learning approach within a psychodynamic psychotherapy sample.
European psychiatry : the journal of the Association of European Psychiatrists
Nov 15, 2024
BACKGROUND: Since its introduction in the diagnostic manuals DSM-5 and ICD-11, the construct of personality functioning has gained increasing attention. However, it remains unclear which factors might predict improvement in personality functioning.
Large-Scale Study on AI's Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging.
Academic radiology
Jul 12, 2024
RATIONALE AND OBJECTIVES: Given the high volume of chest radiographs, radiologists frequently encounter heavy workloads. In outpatient imaging, a substantial portion of chest radiographs show no actionable findings. Automatically identifying these ca...
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