AIMC Topic: Proportional Hazards Models
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All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning.
Clinical interventions in aging
Jan 1, 2025
INTRODUCTION: The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.
Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images.
Medicine
Dec 20, 2024
PURPOSE: To create a nomogram for accurate prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on computed tomography images.
Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.
Medicine
Sep 27, 2024
Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predict...
sparsesurv: a Python package for fitting sparse survival models via knowledge distillation.
Bioinformatics (Oxford, England)
Sep 2, 2024
MOTIVATION: Sparse survival models are statistical models that select a subset of predictor variables while modeling the time until an event occurs, which can subsequently help interpretability and transportability. The subset of important features i...
Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.
The journal of gene medicine
Sep 1, 2024
OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for ...
CoxFNN: Interpretable machine learning method for survival analysis.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Survival analysis plays a pivotal role in healthcare, particularly in analyzing time-to-event data such as in disease progression, treatment efficacy, and drug development. Traditional methods in survival analysis often face a trade-off: they either ...
Personalized prediction of survival rate with combination of penalized Cox models in patients with colorectal cancer.
Medicine
Jun 14, 2024
The investigation into individual survival rates within the patient population was typically conducted using the Cox proportional hazards model. This study was aimed to evaluate the performance of machine learning algorithm in predicting survival rat...
Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.
Cancer medicine
Jun 1, 2024
OBJECTIVE: We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.
Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in a retrospective cohort study with a large sample size. We analyzed the association between depr...
Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.
Technology in cancer research & treatment
Jan 1, 2024
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and ...
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