ArticleBMC medical informatics and decision making2025
Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Immune-inflammatory and metabolic signatures for osteoporosis risk stratification in primary Sjögren's syndrome: development and internal validation of an interpretable machine-learning model.Frontiers in immunology · 2026Article
- Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveThis study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, evaluating its performance, and conducting validations to explore the most suitable machine learning algorithm.
methodsClinical information, including demographic data, examination results, medical history, and laboratory test results, was collected from inpatients with and without osteoporosis. The LASSO algorithm was utilized for feature selection, and multiple machine learning algorithms were applied to calculate the model's accuracy, precision, recall, F1 score, and average precision (AP) value. Receiver operating characteristic (ROC) curves for each algorithm were plotted, and a comprehensive evaluation was conducted to identify the most suitable machine learning model. Finally, the model's predictive accuracy was validated using corresponding information from other patients.
resultsA total of 1063 patients were included; 562 had osteoporosis, and 501 did not. After LASSO feature selection, the most important features for the model's predictive results were determined to be age, height, weight, alkaline phosphatase activity, and osteocalcin. Evaluation of the accuracy, precision, recall, F1 score, and AP value for each algorithm, along with ROC curves, led to the selection of the light gradient boosting machine (LGBM) algorithm as the best algorithm for the model. The validation results confirmed the model's excellent predictive ability.
conclusionThis study established a preliminary diagnostic model for osteoporosis, contributing to increased efficiency in diagnosing the disease.
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Registered trials
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