An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This study aimed at developing and validating externally patient-wise prognostic models of the global fu...
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and ...
Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental...
High-grade gliomas, particularly glioblastoma (MeSH:Glioblastoma), are among the most aggressive and lethal central nervous system tumors, necessitating advanced diagnostic and prognostic strategies. This systematic review and epistemic meta-analysis...
BACKGROUND: Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understand...
BACKGROUND: Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make ...
BACKGROUND: Dysregulated programmed cell death pathways mechanistically contribute to hepatic inflammation and fibrogenesis in non-alcoholic steatohepatitis (NASH). Identification of cell death genes may offer insights into diagnostic and therapeutic...
AIMS: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs us...
PURPOSE: To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.