Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach

Inui, Atsuyuki; Nishimoto, Hanako; Mifune, Yutaka; Yoshikawa, Tomoya; Shinohara, Issei; Furukawa, Takahiro; Kato, Tatsuo; Tanaka, Shunta; Kusunose, Masaya; Kuroda, Ryosuke

doi:10.3390/bioengineering10030277

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Feature importance was used to rank the most important attributes that significantly contribute to the accuracy of the final prediction models [ 32 ]. To better interpret how each feature contributes to the associated outcome, we performed SHAP (SHapley Additive exPlanations) [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

“…When compared to APACHE II and SOFA scores, the p-values obtained were 0.0180 and 0.0156, respectively, indicating significant differences (Table 4). Feature importance was used to rank the most important attributes that significantly contribute to the accuracy of the final prediction models [32]. To better interpret how each feature contributes to the associated outcome, we performed SHAP (SHapley Additive exPlanations) [33].…”

Section: Comparing the Best-performing Model With Traditional Icu Ass...mentioning

confidence: 99%

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Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury

Tu,

Tau,

Chen

et al. 2023

Diagnostics

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Background: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. Method: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. Result: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. Conclusion: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.

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Section: Resultsmentioning

confidence: 99%

Section: Comparing the Best-performing Model With Traditional Icu Ass...mentioning

confidence: 99%

Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury

Tu,

Tau,

Chen

et al. 2023

Diagnostics

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“…LR models are widely used for multivariate analysis. 12,13) During the training process, the hyperparameters were optimized us-ing the grid-search approach.…”

Section: Machine-learning Algorithms and Featuresmentioning

confidence: 99%

Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women

Kang,

Kim,

Hur

et al. 2024

Korean J Fam Med

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Background: Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters.Methods: We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score.Results: Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively.Conclusion: This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.

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A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis

Cheng,

Cai,

et al. 2023

J Bone Miner Metab

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Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach

Cited by 8 publications

References 25 publications

Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury

Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury

Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis

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