AIMC Topic: United States

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Applying machine learning approaches for predicting obesity risk using US health administrative claims database.

BMJ open diabetes research & care Sep 26, 2024
INTRODUCTION: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive predictive value (PPV) of BMI-related diagnosis codes using an electronic medical records (EMR) clai...
Risk Factors Aged Young Adult Adult Male Prognosis Retrospective Studies Humans Electronic Health Records Machine Learning Female Databases, Factual Middle Aged Risk Assessment Adolescent ROC Curve United States Body Mass Index Obesity Administrative Claims, Healthcare
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Artificial intelligence large language model scores highly on focused practice designation in metabolic and bariatric surgery board practice questions.

Surgical endoscopy Sep 25, 2024
BACKGROUND: Artificial intelligence models such as ChatGPT (Open AI) have performed well on the exams of various medical and surgical fields. It is not yet known how ChatGPT performs on similar metabolic and bariatric surgery (MBS) questions.
Surveys and Questionnaires Clinical Competence United States Bariatric Surgery Specialty Boards Humans Artificial Intelligence
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Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study.

JMIR research protocols Sep 25, 2024
BACKGROUND: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes.
Humans Machine Learning Decision Support Techniques Female Male Middle Aged Decision Support Systems, Clinical United States Diabetes Mellitus Social Determinants of Health Case Management
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Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.

Journal of psychiatric research Sep 24, 2024
Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suic...
Aged Risk Assessment Adult Suicide Male Humans United States Middle Aged Machine Learning United States Department of Veterans Affairs Natural Language Processing Electronic Health Records Female Veterans
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Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

EBioMedicine Sep 24, 2024
BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term...
Cohort Studies United States Post-Acute COVID-19 Syndrome Crowdsourcing Machine Learning COVID-19 Algorithms SARS-CoV-2 Humans
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Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.

Expert review of pharmacoeconomics & outcomes research Sep 23, 2024
OBJECTIVES: Adherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.
Humans Diabetes Complications Unsupervised Machine Learning Hypoglycemic Agents Pharmacists Machine Learning Adult Insurance, Health Diabetes Mellitus Professional Role Female Age Factors Male Self Report Pharmaceutical Services Aged Cluster Analysis Middle Aged Sex Factors Surveys and Questionnaires United States
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Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.

COPD Sep 23, 2024
Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical reco...
Insurance Claim Review alpha 1-Antitrypsin Deficiency Aged Male Humans Middle Aged Machine Learning United States Databases, Factual Electronic Health Records Female
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Artificial Intelligence Large Language Models Address Anterior Cruciate Ligament Reconstruction: Superior Clarity and Completeness by Gemini Compared With ChatGPT-4 in Response to American Academy of Orthopaedic Surgeons Clinical Practice Guidelines.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association Sep 21, 2024
PURPOSE: To assess the ability of ChatGPT-4 and Gemini to generate accurate and relevant responses to the 2022 American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guidelines (CPG) for anterior cruciate ligament reconstruction (ACLR).
Humans Artificial Intelligence Language Anterior Cruciate Ligament Injuries Anterior Cruciate Ligament Reconstruction Large Language Models Orthopedics Orthopedic Surgeons Surveys and Questionnaires Generative Artificial Intelligence United States Practice Guidelines as Topic
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Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery Sep 21, 2024
PURPOSE: Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions. This study aimed to develop a...
United States SEER Program Male Middle Aged Thyroid Neoplasms Thyroid Carcinoma, Anaplastic Humans Machine Learning Algorithms Prognosis Female Aged Survival Rate
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Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis.

JACC. Cardiovascular imaging Sep 18, 2024
BACKGROUND: The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict.
Humans Prognosis Female Ventricular Function, Left Echocardiography Middle Aged Risk Assessment Disease Progression Reproducibility of Results Risk Factors Aged Male Diastole Sclerosis Calcinosis Aortic Valve Stenosis Aortic Valve Predictive Value of Tests Ventricular Dysfunction, Left Severity of Illness Index Deep Learning Time Factors United States
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