AIMC Topic: Cross-Sectional Studies

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Presenting a prediction model for HELLP syndrome through data mining.

BMC medical informatics and decision making
BACKGROUND: The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathogenesis of HELLP syndrome are not yet fully known and well understood, distinguishing it from other pr...

Defining lipedema's molecular hallmarks by multi-omics approach for disease prediction in women.

Metabolism: clinical and experimental
Lipedema is a chronic disease in females characterized by pathologic subcutaneous adipose tissue expansion and hitherto remains without druggable targets. In this observational study, we investigated the molecular hallmarks of lipedema using an unbia...

Association between muscle mass assessed by an artificial intelligence-based ultrasound imaging system and quality of life in patients with cancer-related malnutrition.

Nutrition (Burbank, Los Angeles County, Calif.)
INTRODUCTION: Emerging evidence suggests that diminished skeletal muscle mass is associated with lower health-related quality of life (HRQOL) in individuals with cancer. There are no studies that we know of in the literature that use ultrasound syste...

Assessing GPT-4's accuracy in answering clinical pharmacological questions on pain therapy.

British journal of clinical pharmacology
AIMS: This study aimed to evaluate the accuracy and completeness of GPT-4, a large language model, in answering clinical pharmacological questions related to pain therapy, with a focus on its potential as a tool for delivering patient-facing medical ...

A sentiment analysis of YouTube videos from donor-conceived people, utilizing artificial intelligence (ChatGPT).

Reproductive biomedicine online
RESEARCH QUESTION: What is the tone of donor-conceived people (DCP) towards their donor-conceived status in YouTube videos assessed for sentiment analysis via ChatGPT, a natural language-processing tool?

Awareness of the Role of Artificial Intelligence in Health Care among Undergraduate Nursing Students: A Descriptive Cross-Ssectional Study.

Nurse education today
BACKGROUND: Artificial intelligence (AI) has the potential to revolutionize healthcare by improving efficiency and reducing errors; however, challenges such as inadequate funding and lack of awareness among healthcare professionals hinder its integra...

Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.

BMC medical informatics and decision making
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease r...

A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.

Psychiatric genetics
BACKGROUND: Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medication...