AIMC Topic: Proportional Hazards Models
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Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Aug 14, 2024
INTRODUCTION: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development.
Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.
BMC infectious diseases
Aug 9, 2024
BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accur...
Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer.
Journal of translational medicine
Aug 5, 2024
Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical...
Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Aug 4, 2024
OBJECTIVES: Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use...
Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.
Head & neck
Jul 30, 2024
BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (...
Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.
Scientific reports
Jul 29, 2024
The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretabl...
A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.
Journal of cancer research and clinical oncology
Jul 25, 2024
PURPOSE: Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.
Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.
PloS one
Jul 24, 2024
BACKGROUND: This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clin...
Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods.
BMC medical research methodology
Jul 23, 2024
BACKGROUND: In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual's risk of e...
Evaluation of risk factors and survival rates of patients with early-stage breast cancer with machine learning and traditional methods.
International journal of medical informatics
Jul 11, 2024
BACKGROUND: This article is aimed to make predictions in terms of prognostic factors and compare prediction methods by using Cox proportional hazards regression analysis (CPH), some machine learning techniques and Accelerated Failure Time (AFT) model...
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