Risk Assessment of Hip Fracture Based on Machine Learning
Joint Authors
Galassi, Alessio
Martín-Guerrero, José D.
Villamor, Eduardo
Monserrat, Carlos
Rupérez, María José
Source
Applied Bionics and Biomechanics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract 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%.
American Psychological Association (APA)
Galassi, Alessio& Martín-Guerrero, José D.& Villamor, Eduardo& Monserrat, Carlos& Rupérez, María José. 2020. Risk Assessment of Hip Fracture Based on Machine Learning. Applied Bionics and Biomechanics،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1120227
Modern Language Association (MLA)
Galassi, Alessio…[et al.]. Risk Assessment of Hip Fracture Based on Machine Learning. Applied Bionics and Biomechanics No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1120227
American Medical Association (AMA)
Galassi, Alessio& Martín-Guerrero, José D.& Villamor, Eduardo& Monserrat, Carlos& Rupérez, María José. Risk Assessment of Hip Fracture Based on Machine Learning. Applied Bionics and Biomechanics. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1120227
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1120227