A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study.

ABSTRACT:

Background

Identifying persons with a high risk of developing osteoporosis and preventing the occurrence of the first fracture is a health care priority. Most existing osteoporosis screening tools have high sensitivity but relatively low specificity.

Objective

We aimed to develop an easily accessible and high-performance preclinical risk screening tool for osteoporosis using a machine learning-based method among the Hong Kong Chinese population.

Methods

Participants aged 45 years or older were enrolled from 6 clinics in the 3 major districts of Hong Kong. The potential risk factors for osteoporosis were collected through a validated, self-administered questionnaire and then filtered using a machine learning-based method. Bone mineral density was measured with dual-ene

SUBMITTER: Yang Q 

PROVIDER: S-EPMC10686208 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature