Obesity

Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-management, and support clinical decision making. Longitudinal Scottish primary care data for 21,250 asthma patients were used to predict the risk of as...

Predicting Weight Loss Success After Gastric Sleeve Surgery: A Machine Learning-Based Approach.

BACKGROUND/OBJECTIVES: Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of tools to predict body weight loss success. The main objective of this paper is to evaluate predictive variables for successful weight loss one year after...

Coptidis rhizoma and berberine as anti-cancer drugs: A 10-year updates and future perspectives.

Cancer continues to be among the most substantial health challenges globally. Among various natural compounds, berberine, an isoquinoline alkaloid obt...

Intelligent Care: A Scientometric Analysis of Artificial Intelligence in Precision Medicine.

The integration of advanced computational methods into precision medicine represents a transformative advancement in healthcare, enabling highly perso...

Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.

The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, a...

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.

BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal ca...

Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular ...

Changes in recreational drug use, reasons for those changes and their consequence during and after the COVID-19 pandemic in the UK.

Changes in drug use in the general population during the COVID-19 pandemic and their long-term consequences are not well understood. We employed natur...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transforma...

RCPE in association with the American College of Gastroenterology and the Scottish Society of Gastroenterology - Gastroenterology: A global perspective.

On 6 November 2024, the Royal College of Physicians of Edinburgh (RCPE) hosted its annual gastroenterology symposium, marking the first collaboration ...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few stu...

Structural brain pattern abnormalities in tinnitus with and without hearing loss.

OBJECTIVE: Subjective tinnitus often coexists with hearing loss, and they share common pathophysiological mechanisms. This comorbidity induces whole-b...

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteriosclerotic occlusion disease (ASO) in the lower ex...

AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity.

OBJECTIVE: Although patients prefer describing their problems using words, mental health interventions are commonly evaluated using rating scales. For...

Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysregulation, including obesity, elevated blood glucose...

Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.

The aim of this study was to establish the optimal prediction model by comparing the prediction effect of 6 kinds of prediction models containing bioc...

Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.

OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marro...

Effectiveness of a community intervention program on healthy lifestyles (PREDICOL) among adults with prediabetes in two Latin American cities: A quasi-experimental study.

PURPOSE: This study aimed to measure the impact of a community-based lifestyle modification intervention program on the Health-Related Quality of Life...

Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.

The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney di...

From acute to chronic low back pain: the role of negative emotions.

Low back pain (LBP) constitutes a persistent and burdensome condition, frequently intricately intertwined with negative emotional states. The focus of...