Injury Prediction
1 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model).
Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units
This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction.
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
Intimate Partner Violence and Injury Prediction From Radiology Reports
Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue.
Low-rank representation of head impact kinematics: A data-driven emulator
In characterizing our existing data set of 537 head impacts, consisting of 6 degrees of freedom measurements, we found that only a few modes, e. g. 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set.
AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes
This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted from attributes with machine learning.
A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.
Concussion classification via deep learning using whole-brain white matter fiber strains
Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e. g., average accuracy of 0. 844 vs. 0. 746, and average area under the receiver operating curve (AUC) of 0. 873 vs. 0. 769, respectively, based on the testing dataset).
Predictive modelling of training loads and injury in Australian football
Focusing the modelling approach on specific injury types and increasing the amount of training data may lead to the development of improved predictive models for injury prevention.
Predictive modelling of football injuries
Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football.
