USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.

TitleUSING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.
Publication TypeJournal Article
Year of Publication2023
AuthorsWickersham M, Bartelo N, Kulm S, Liu Y, Zhang Y, Elemento O
JournalRes Sq
Date Published2023 Oct 03
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.

DOI10.21203/rs.3.rs-3154584/v1
Alternate JournalRes Sq
PubMed ID37886491
PubMed Central IDPMC10602059
Grant ListR01 CA194547 / CA / NCI NIH HHS / United States
U24 CA210989 / CA / NCI NIH HHS / United States
UL1 TR002384 / TR / NCATS NIH HHS / United States
P50 CA211024 / CA / NCI NIH HHS / United States
T32 GM007739 / GM / NIGMS NIH HHS / United States

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