AIMC Topic: Survival Analysis

Clear Filters Showing 211 to 220 of 331 articles

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

Depression and anxiety
BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital disc...

Molecular and epigenetic profiles of BRCA1-like hormone-receptor-positive breast tumors identified with development and application of a copy-number-based classifier.

Breast cancer research : BCR
BACKGROUND: BRCA1-mutated cancers exhibit deficient homologous recombination (HR) DNA repair, resulting in extensive copy number alterations and genome instability. HR deficiency can also arise in tumors without a BRCA1 mutation. Compared with other ...

Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta-analysis.

Injury
BACKGROUND: Currently, two models of artificial neural network (ANN) and logistic regression (LR) are known as models that extensively used in medical sciences. The aim of this study was to compare the ANN and LR models in prediction of Health-relate...

Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.

American journal of obstetrics and gynecology
BACKGROUND: Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. How...

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme.

Cancer medicine
BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, patholog...

Defining Massive Transfusion in Civilian Pediatric Trauma With Traumatic Brain Injury.

The Journal of surgical research
The purpose of this study was to identify an optimal definition of massive transfusion in civilian pediatric trauma with severe traumatic brain injury (TBI) METHODS: Severely injured children (age ≤18 y) with severe TBI in the Trauma Quality Improvem...

Disparities in the Treatment and Outcome of Stage I Non-Small-Cell Lung Cancer in the 21st Century.

Clinical lung cancer
BACKGROUND: African American (AA) individuals are less likely to receive treatment and more likely to die from cancer compared with Caucasian (C) individuals. Recent advancements in surgery and radiation have improved outcomes in early stage non-smal...

A data-driven artificial intelligence model for remote triage in the prehospital environment.

PloS one
In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical pe...

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease.

PloS one
Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require signifi...

Automated Gleason grading of prostate cancer tissue microarrays via deep learning.

Scientific reports
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibili...