Risk Assessment of Hip Fracture Based on Machine Learning

Autor: Eduardo Villamor, M. J. Rupérez, Carlos Monserrat, Alessio Galassi, José D. Martín-Guerrero
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0301 basic medicine
Article Subject
Process (engineering)
Computer science
QH301-705.5
INGENIERIA MECANICA
media_common.quotation_subject
Osteoporosis
Biomedical Engineering
Medicine (miscellaneous)
030209 endocrinology & metabolism
Bioengineering
Machine learning
computer.software_genre
Risk Assessment
Machine Learning
03 medical and health sciences
Hip Fracture
0302 clinical medicine
medicine
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
Sensitivity (control systems)
Biology (General)
media_common
Hip fracture
Variables
business.industry
Gold standard (test)
medicine.disease
Random forest
030104 developmental biology
Artificial intelligence
Risk assessment
business
LENGUAJES Y SISTEMAS INFORMATICOS
computer
TP248.13-248.65
Research Article
Biotechnology
Zdroj: Applied Bionics and Biomechanics, Vol 2020 (2020)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Applied Bionics and Biomechanics
ISSN: 1754-2103
1176-2322
Popis: [EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.
This study was partially funded by the FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor.
Databáze: OpenAIRE
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