AIMC Topic: Prognosis

Clear Filters Showing 111 to 120 of 3837 articles

The ferroptosis-related gene MAFG screened by machine learning is associated with the diagnosis and prognosis of sepsis.

Clinical and experimental medicine
Ferroptosis is a novel form of cell death induced by ferrous ions and lipid peroxidation. However, the mechanisms of ferroptosis-related genes (FRGs) in sepsis have not been studied thoroughly. We performed differential analysis using GSE65682, and t...

Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.

Journal of neuro-oncology
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...

BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.

Journal of translational medicine
BACKGROUND: Breast cancer is one of the most prevalent malignancies globally, imposing a substantial disease burden. Its inherent heterogeneity complicates prognosis and treatment, underscoring the need for accurate survival prediction models to guid...

Comprehensive analysis of coagulation-associated gene signature in bladder cancer diagnosis, prognosis, and immunotherapy.

Scientific reports
In recent years, research on the relationship between coagulation system abnormalities and tumor immunity has been widely reported. Bladder cancer (BC), as an immunogenic tumor, holds great promise in immunotherapy. The role of coagulation-related ge...

Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol.

BMJ open
INTRODUCTION: To enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) al...

Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest.

BMC research notes
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...

MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis.

Human genomics
BACKGROUND: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aim...

The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning.

BMC cancer
BACKGROUND: In multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.

Comparison of serum lactate and lactate-derived ratios as prognostic biomarkers in pediatric dengue shock syndrome using supervised machine learning models.

PloS one
BACKGROUND: Dengue shock syndrome (DSS), with critical complications encompassing mechanical ventilation (MV), dengue-associated acute liver failure (PALF), and encephalitis, is associated with high mortality in children. Although serum lactate is a ...

Deciphering lactate/lactylation networks in AML: integrated scRNA-seq and transcriptomics reveal functions and prognostic model.

BMC cancer
Acute myeloid leukemia (AML) exhibits pronounced heterogeneity, necessitating deep molecular characterization for precision therapy. Lactate metabolism and histone lactylation, influencing tumor biology via epigenetic regulation and immune microenvir...