JMIR Publications

ABSTRACT

Background:

Current Atherosclerotic cardiovascular disease (ASCVD) predictive models have limitations, efforts are underway to improve the discriminatory power of ASCVD models.

Objective:

We sought to evaluate the discriminatory power of using social media posts to predict 10-year risk for ASCVD as compared to the pooled cohort risk equations (PCEs)

Methods:

We consented patients receiving care in an urban academic emergency department to share access to their Facebook posts and electronic medical records (EMR). We retrieved Facebook status updates up to 5-years prior to study enrollment for all consenting patients. We identified patients (n=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine learning (ML) model to predict 10-year ASCVD risk scores. Using a ML model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count (LIWC) we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively.

Results:

A ML model predicted the 10-year ASCVD risk scores for these categories: <5%, 5% - 7.4%, 7.5% - 9.9%, and >=10% with AUC’s: 0.78, 0.57, 0.72, and 0.61, respectively. A ML model distinguished between low risk (<10%) and high risk (>10%) with an AUC of 0.69. Additionally, a ML model predicted the ASCVD risk score with Pearson’s r = 0.26. Using LIWC, patients with higher ASCVD scores were more likely to use words associated with sadness (Pearson’s r = 0.32).

Conclusions:

Language used on social media can provide insights about an individual’s ASCVD risk and inform approaches to risk modification.