JMIR Publications

ABSTRACT

Background:

Fractures due to osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term, and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years.

Objective:

The goal of this study was to develop and evaluate an algorithmic approach to the identification of patients at risk of fracture in the next 1-2 years. In order to address the aforementioned limitations of current prediction tools, this approach focuses on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions.

Methods:

Using electronic health record (EHR) data, we developed Crystal Bone, a method that applies machine learning techniques from Natural Language Processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the “signature” of the patient’s journey is similar to that of a typical future fracture patient.

Results:

The proposed models accurately predict fracture risk in the next 1-2 years for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC]=0.81). These algorithms outperform the experimental baselines (AUROC=0.67) and have shown meaningful improvements when compared to a retrospective approximation of human-level performance, such as correctly identifying 70% of at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians.

Conclusions:

These findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the healthcare system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.