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Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal of health, population, and nutrition
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...

Machine learning-based prediction of respiratory depression during sedation for liposuction.

Scientific reports
Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation...

UNIK (Urologic Non-Neoplastic Investigation of Kidneys): a machine learning approach to decode benign lesion.

World journal of urology
PURPOSE: Predicting the likelihood of benign neoplasia in patients with suspected renal cell carcinoma (RCC) is a cornerstone of presurgical planning. We sought to create and validate U.N.I.K., a machine learning (ML) model capable of predicting beni...

Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.

Scientific reports
Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and early prediction of sAKI has an important role in timely intervention, ultimately improving the patients...

Identification of key proteins and pathways in myocardial infarction using machine learning approaches.

Scientific reports
Acute myocardial infarction (AMI) is a leading cause of global morbidity and mortality, requiring deeper insights into its molecular mechanisms for improved diagnosis and treatment. This study combines proteomics, transcriptomics and machine learning...

Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.

Scientific reports
Urethral recurrence (UR) following radical cystectomy for bladder cancer represents an aggressive disease failure with typically poor survival outcomes. Our study aimed to assess the predictive risk factors for UR, to develop and validate an easy-to-...

Digital removal of dermal denticle layer using geometric AI from 3D CT scans of shark craniofacial structures enhances anatomical precision.

Scientific reports
Craniofacial morphometrics in sharks provide crucial insights into evolutionary history, geographical variation, sexual dimorphism, and developmental patterns. However, the fragile cartilaginous nature of shark craniofacial skeleton poses significant...

Clinical Management of Wasp Stings Using Large Language Models: Cross-Sectional Evaluation Study.

Journal of medical Internet research
BACKGROUND: Wasp stings are a significant public health concern in many parts of the world, particularly in tropical and subtropical regions. The venom of wasps contains a variety of bioactive compounds that can lead to a wide range of clinical effec...

Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.

JMIR formative research
BACKGROUND: Nurse scheduling is a complex challenge in health care, impacting both patient care quality and nurse well-being. Traditional scheduling methods often fail to consider individual preferences, leading to dissatisfaction, burnout, and high ...

Determining the Requirements of Vulnerable Groups for Health Counseling and Optimizing the Evaluation of Health Consultations: Mixed Methods Study With the Use of AI.

JMIR formative research
BACKGROUND: Evaluating health counseling services is crucial for ensuring their quality and effectiveness. However, this process is hampered by challenges such as language barriers and limited awareness of their needs and concerns.