AIMC Topic: Proportional Hazards Models

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Evaluating eligibility criteria of oncology trials using real-world data and AI.

Nature Apr 7, 2021
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes ...
Humans Artificial Intelligence Patient Safety Medical Oncology Datasets as Topic Clinical Trials as Topic Proportional Hazards Models Clinical Laboratory Techniques Electronic Health Records Reproducibility of Results Lung Neoplasms Carcinoma, Non-Small-Cell Lung Patient Selection
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A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters.

Cancer medicine Mar 24, 2021
PURPOSE: Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high-grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians...
Proportional Hazards Models Karnofsky Performance Status Brain Neoplasms Male Middle Aged Models, Theoretical Treatment Outcome Glioma Support Vector Machine Magnetic Resonance Imaging Area Under Curve Combined Modality Therapy Humans Machine Learning Prognosis Female Aged
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Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare.

American journal of nephrology Mar 19, 2021
BACKGROUND: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare.
Adult Male Humans Risk Assessment Recurrence Clinical Decision-Making Age Factors Machine Learning Symptom Flare Up Female Models, Statistical Capillaries Lupus Nephritis Risk Factors Antibodies, Antinuclear Proportional Hazards Models Young Adult Complement C3 Kaplan-Meier Estimate Serum Albumin
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Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.

BMC cancer Mar 16, 2021
BACKGROUND: The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural n...
Retrospective Studies Proportional Hazards Models Female Predictive Value of Tests Liver Neoplasms Postoperative Period Risk Factors Humans Neoplasm Staging Carcinoma, Hepatocellular Male Neural Networks, Computer Neoplasm Recurrence, Local Nomograms Middle Aged Disease-Free Survival Follow-Up Studies Hepatectomy Liver
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The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types.

EBioMedicine Mar 9, 2021
BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma frac...
Proportional Hazards Models Prognosis Retrospective Studies Neoplasms Humans Survival Analysis Machine Learning Stromal Cells
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Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system.

Aging Mar 3, 2021
We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained ...
Carcinoma, Renal Cell Genes, Neoplasm Models, Statistical Survival Analysis Kidney Neoplasms Proportional Hazards Models Kaplan-Meier Estimate Humans Artificial Intelligence Algorithms Prognosis Female Male Middle Aged Case-Control Studies Nomograms
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Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

Circulation. Heart failure Feb 9, 2021
BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment...
Humans Heart Failure Cardiovascular Diseases Machine Learning Middle Aged Coronary Disease Heart Disease Risk Factors Female Red Meat Diet Aged Milk Protective Factors Incidence Animals Coffee Male Stroke Proportional Hazards Models
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Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model.

BMC bioinformatics Feb 5, 2021
BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a...
Humans Neoplasms Neural Networks, Computer Deep Learning Survival Analysis Proportional Hazards Models
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Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.

BMC cardiovascular disorders Jan 18, 2021
BACKGROUND: Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of f...
Time Factors Treatment Outcome Percutaneous Coronary Intervention Coronary Artery Disease Iran Stents Cerebrovascular Disorders Heart Diseases Proportional Hazards Models Risk Factors Aged Male Middle Aged Risk Assessment Machine Learning Decision Support Techniques Humans Retrospective Studies Female
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Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

JAMA network open Dec 1, 2020
IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (...
Predictive Value of Tests Neoplasm Staging Mutation Survival Analysis Proportional Hazards Models Humans Male Machine Learning ErbB Receptors Middle Aged Prognosis Antineoplastic Agents Protein Kinase Inhibitors Female Lung Neoplasms Antineoplastic Protocols Carcinoma, Non-Small-Cell Lung
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