Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML...
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of features is significantly larger than the sample size, making the integration of multi-omics data for survival analysis of a specific cancer particularly ...
Accurate cancer survival prediction plays a crucial role in assisting clinicians in formulating treatment plans. Multimodal data, such as histopathological images, genomic data, and clinical information, provide complementary and comprehensive inform...
BMC medical informatics and decision making
40119422
BACKGROUND: Survival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making...
Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit signific...
BMC medical informatics and decision making
40251623
The automated processing of Electronic Health Records (EHRs) poses a significant challenge due to their unstructured nature, rich in valuable, yet disorganized information. Natural Language Processing (NLP), particularly Named Entity Recognition (NER...
Studies in health technology and informatics
40200468
Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers for predicting survival in patients with metastatic castration-resistant prostate cancer (mCRPC) sti...
Studies in health technology and informatics
40200453
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting s...
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The incidence of new cases has been increasing in recent years. Developing a predictive model to forecast t...
We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 gra...