Using Machine Learning Methods to Assess the Risk of Alcohol Misuse in Older Adults

Abstract The population of older adults, defined in this study as those 50 years of age or older, continues to increase every year. Substance misuse, particularly alcohol misuse, is often neglected in these individuals. To better identify older adults who might not be properly assessed for alcohol misuse, we have derived a risk assessment tool using patients from the United Kingdom Biobank (UKB), which was validated on patients in the Weill Cornell Medicine (WCM) electronic health record (EHR). The model and tooling created stratifies the risk of alcohol misuse in older adults using 10 features that are commonly found in most EHR systems. We found that the area under the receiver operating curve (AUROC) to correctly predict alcohol misuse in older adults for the UKB and WCM models were 0.84 and 0.78, respectively. We further show that of those who self-identified as having ongoing alcohol misuse in the UKB cohort, only 12.5% of these patients had any alcohol-related F.10 ICD-10 code. Extending this to the WCM cohort, we forecast that 7,838 out of 12,360 older adults with no F.10 ICD-10 code (63.4%) may be missed as having alcohol misuse in the EHR. Overall, this study importantly prioritizes the health of older adults by being able to predict alcohol misuse in an understudied population.