AIMC Topic: Middle Aged

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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BMC cardiovascular disorders
BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an elevated risk of developing cardiovascular diseases, including carotid atherosclerosis. While machi...

Diagnosis of psoriasis and lichen planus in real-time using neural networks based on skin Biomechanical properties obtained from numerical simulation.

Scientific reports
Due to their similar clinical presentations, the scarcity of competent dermatologists, and the urgency of diagnosis, the accurate diagnosis of dermatological conditions such as Psoriasis and Lichen Planus is challenging. This study introduces a novel...

Simultaneous determination of 7 thiols associated proteins in lymphoma patients'serum and cerebrospinal fluid by UHPLC-HRMS technique.

Scientific reports
Thiol compounds can serve as markers for the antioxidant and prognostic status of lymphoma, playing a crucial role in early tumor diagnosis. However, their high polarity and lack of chromophores pose challenges for multivariate analysis. This study a...

Machine learning-based prediction of celiac antibody seropositivity by biochemical test parameters.

Scientific reports
The diagnostic delay in celiac disease (CD) is currently a burden for individual and society. Biochemical tests may be used in risk-identification of CD to reduce the diagnostic delay, and we aimed to explore prediction models for CD antibody seropos...

Machine learning algorithms for prediction of cerebrospinal fluid leakage after posterior surgery for thoracic ossification of the ligamentum flavum.

Scientific reports
To develop and validate a machine-learning (ML) model that pre-operatively predicts cerebrospinal-fluid leakage (CSFL) after posterior decompression for thoracic ossification of the ligamentum flavum (TOLF), and to elucidate the key risk factors driv...

Can ChatGPT Provide Patient-Friendly and Reliable Information on Cervical Cancer Screening? A Study of ChatGPT-Generated Information in Polish.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Cervical cancer (CC) mortality remains a global health problem, and women's awareness of the need for regular CC screening is insufficient. In the era of rapid development of artificial intelligence (AI), large language models (LLMs) such ...

Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.

Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease.

BMC psychiatry
BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often accompanied by depression, which exacerbates disease burden and impairs quality of life. Early identification of depression risk in COPD patients rema...

Solicitude toward artificial intelligence among health care providers and its relation to their patient's safety culture in Saudi Arabia.

BMC health services research
BACKGROUND: The healthcare sector is undergoing a digital transformation, where the integration of Artificial Intelligence (AI) plays a vital role in reshaping healthcare practices. AI technologies promise to improve work procedures, mitigate future ...

Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis.

Respiratory research
BACKGROUND: The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the urgency advanced predictive tools. This study aimed to develop a machine learning-based predictive ...