Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.
OBJECTIVE:Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrativ...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0232969 |
| _version_ | 1818833543979073536 |
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| author | Alexander Engels Katrin C Reber Ivonne Lindlbauer Kilian Rapp Gisela Büchele Jochen Klenk Andreas Meid Clemens Becker Hans-Helmut König |
| author_facet | Alexander Engels Katrin C Reber Ivonne Lindlbauer Kilian Rapp Gisela Büchele Jochen Klenk Andreas Meid Clemens Becker Hans-Helmut König |
| author_sort | Alexander Engels |
| collection | DOAJ |
| description | OBJECTIVE:Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods. METHODS:We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance. RESULTS:All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set. CONCLUSIONS:The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets. |
| first_indexed | 2024-12-19T02:20:36Z |
| format | Article |
| id | doaj.art-5ecfbab5f8f644b8aff29508ff7c35fa |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T02:20:36Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5ecfbab5f8f644b8aff29508ff7c35fa2022-12-21T20:40:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01155e023296910.1371/journal.pone.0232969Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.Alexander EngelsKatrin C ReberIvonne LindlbauerKilian RappGisela BücheleJochen KlenkAndreas MeidClemens BeckerHans-Helmut KönigOBJECTIVE:Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods. METHODS:We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance. RESULTS:All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set. CONCLUSIONS:The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets.https://doi.org/10.1371/journal.pone.0232969 |
| spellingShingle | Alexander Engels Katrin C Reber Ivonne Lindlbauer Kilian Rapp Gisela Büchele Jochen Klenk Andreas Meid Clemens Becker Hans-Helmut König Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. PLoS ONE |
| title | Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. |
| title_full | Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. |
| title_fullStr | Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. |
| title_full_unstemmed | Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. |
| title_short | Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach. |
| title_sort | osteoporotic hip fracture prediction from risk factors available in administrative claims data a machine learning approach |
| url | https://doi.org/10.1371/journal.pone.0232969 |
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