Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications. This study aimed to evaluate fundus photographs using artificial intelligence to explore the relat...
This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the type of anticoagulation therapy in geriatric patients with traumatic brain injury. A retrospective ana...
This study evaluated the performance of ChatGPT with GPT-4 Omni (GPT-4o) on the 118th Japanese Medical Licensing Examination. The study focused on both text-only and image-based questions. The model demonstrated a high level of accuracy overall, with...
BACKGROUND: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psychological, behavioral, and demographic factors. When analyzing these factors, challenges such as mul...
International journal of clinical oncology
39714567
BACKGROUND: The implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-case reviews by Molecular Tumor Boards (MTBs), which impose considerable operational burdens on heal...
Journal of the American Medical Directors Association
39701553
OBJECTIVES: The study aimed to develop a machine learning (ML) model to predict early postdischarge falls in older adults using data that are easy to collect in acute care hospitals. This may reduce the burden imposed by complex measures on patients ...
BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategie...
STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.
Clinical and experimental hypertension (New York, N.Y. : 1993)
39773295
OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the devel...