Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

(2020) Atif Mehmood et al.   Brain Sciences

An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain

(2019) Chieh-Chen Wu et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Real-World Evidence, Causal Inference, and Machine Learning

(2019) William H. Crown   VALUE IN HEALTH

Artificial neural network optimizes self-examination of osteoporosis risk in women

(2019) Jia Meng et al.   JOURNAL OF INTERNATIONAL MEDICAL RESEARCH

Predictive analytics with gradient boosting in clinical medicine

(2019) Zhongheng Zhang et al.   Annals of Translational Medicine

Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion

(2018) Jun S. Kim et al.   SPINE

A systematic study of the class imbalance problem in convolutional neural networks

(2018) Mateusz Buda et al.   NEURAL NETWORKS

Advances in osteoporosis from 1970 to 2018

(2018) J. Christopher Gallagher   MENOPAUSE-THE JOURNAL OF THE NORTH AMERICAN MENOPAUSE SOCIETY

European guidance for the diagnosis and management of osteoporosis in postmenopausal women

(2018) J.A. Kanis et al.   OSTEOPOROSIS INTERNATIONAL

Update on Osteoporosis

(2018) Srikala Yedavally-Yellayi et al.   PRIMARY CARE

Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

(2018) Hyung-Chul Lee et al.   Journal of Clinical Medicine

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

(2018) Hyung-Chul Lee et al.   Journal of Clinical Medicine

Prediction of fatty liver disease using machine learning algorithms

(2018) Chieh-Chen Wu et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Prediction of lung cancer patient survival via supervised machine learning classification techniques

(2017) Chip M. Lynch et al.   INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Effectiveness of a two-step population-based osteoporosis screening program using FRAX: the randomized Risk-stratified Osteoporosis Strategy Evaluation (ROSE) study

(2017) K. H. Rubin et al.   OSTEOPOROSIS INTERNATIONAL

Predicting the Future — Big Data, Machine Learning, and Clinical Medicine

(2016) Ziad Obermeyer et al.   NEW ENGLAND JOURNAL OF MEDICINE

Postmenopausal Osteoporosis

(2016) Dennis M. Black et al.   NEW ENGLAND JOURNAL OF MEDICINE

BFH-OST, a new predictive screening tool for identifying osteoporosis in postmenopausal Han Chinese women

(2016) Qi Fei et al.   Clinical Interventions in Aging

The Risk-Stratified Osteoporosis Strategy Evaluation study (ROSE): A Randomized Prospective Population-Based Study. Design and Baseline Characteristics

(2015) Katrine Hass Rubin et al.   CALCIFIED TISSUE INTERNATIONAL

Data Resource Profile: The Korea National Health and Nutrition Examination Survey (KNHANES)

(2014) S. Kweon et al.   INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Clinician’s Guide to Prevention and Treatment of Osteoporosis

(2014) F. Cosman et al.   OSTEOPOROSIS INTERNATIONAL

The clinical diagnosis of osteoporosis: a position statement from the National Bone Health Alliance Working Group

(2014) E. S. Siris et al.   OSTEOPOROSIS INTERNATIONAL

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

(2013) Tae Keun Yoo et al.   YONSEI MEDICAL JOURNAL

Postmenopausal osteoporosis

(2013) Dima L. Diab et al.   Current Opinion in Endocrinology Diabetes and Obesity

Management of osteoporosis in postmenopausal women

(2010) &NA;   MENOPAUSE-THE JOURNAL OF THE NORTH AMERICAN MENOPAUSE SOCIETY

Assessment of Postmenopausal Women and Significant Risk Factors for Osteoporosis

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