AIMC Topic: Proportional Hazards Models
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Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.
Clinical rheumatology
Jun 28, 2024
OBJECTIVE: The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and va...
Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.
International journal of colorectal disease
Jun 26, 2024
BACKGROUND: The 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox reg...
Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database.
Computer methods and programs in biomedicine
Jun 25, 2024
BACKGROUND: Studies have found that first primary cancer (FPC) survivors are at high risk of developing second primary breast cancer (SPBC). However, there is a lack of prognostic studies specifically focusing on patients with SPBC.
Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.
Scientific reports
Jun 24, 2024
Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SE...
deepAFT: A nonlinear accelerated failure time model with artificial neural network.
Statistics in medicine
Jun 18, 2024
The Cox regression model or accelerated failure time regression models are often used for describing the relationship between survival outcomes and potential explanatory variables. These models assume the studied covariates are connected to the survi...
Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis.
Scientific reports
Jun 9, 2024
Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and...
Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.
Biological psychiatry
Jun 7, 2024
BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for chan...
A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method.
Computers in biology and medicine
May 28, 2024
BACKGROUND: Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.
Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations.
Nutrients
May 28, 2024
This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced artificial intelligence methodologies. Data from the National Health and Nutrition Examination Surv...
Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate.
Artificial intelligence in medicine
May 23, 2024
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships. In particular, we model each subject as a distribution over "topics...
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