AIMC Topic: Proportional Hazards Models
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Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.
PloS one
Dec 13, 2024
With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by prov...
Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques.
Scientific reports
Dec 2, 2024
HIV remains a critical global health issue, with an estimated 39.9 million people living with the virus worldwide by the end of 2023 (according to WHO). Although the epidemic's impact varies significantly across regions, Africa remains the most affec...
Predicting the time to get back to work using statistical models and machine learning approaches.
BMC medical research methodology
Nov 29, 2024
BACKGROUND: Whether machine learning approaches are superior to classical statistical models for survival analyses, especially in the case of lack of proportionality, is unknown.
Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.
Genes
Nov 12, 2024
Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of breast cancer patients warrants further study. The anoikis-related prognostic signature (ANRS) was gene...
Proportional Hazards Models
Anoikis
Drug Screening Assays, Antitumor
Gene Expression
Blotting, Western
Imatinib Mesylate
Enzyme-Linked Immunosorbent Assay
Treatment Outcome
Databases, Genetic
Predictive Value of Tests
Tumor Microenvironment
Biomarkers, Tumor
Middle Aged
Antineoplastic Agents
Real-Time Polymerase Chain Reaction
Reverse Transcriptase Polymerase Chain Reaction
Immunotherapy
Gene Expression Profiling
Transcriptome
Female
Reproducibility of Results
Aged
Breast Neoplasms
Machine Learning
Prognosis
Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.
Computer methods and programs in biomedicine
Nov 8, 2024
BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.
Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.
Health care management science
Nov 4, 2024
BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in healthcare in America. For cancer specifically, one of the leading observed disparities is worse mortality among non-Hispanic Black patients compared to n...
Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.
BMC gastroenterology
Oct 31, 2024
BACKGROUND: Deep learning has made significant advancements in the field of digital pathology, and the integration of multiple models has further improved accuracy. In this study, we aimed to construct a combined prognostic model using deep learning-...
Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.
Journal of imaging informatics in medicine
Oct 24, 2024
To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep le...
Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss.
Statistics in medicine
Oct 12, 2024
An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure tim...
Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.
Computer methods and programs in biomedicine
Sep 29, 2024
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...
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