Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study - Takeaways - MDSpire

Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study

  • By

  • Novalene Alsenay Goklish

  • Emily E Haroz

  • Rohan R Dayal

  • Valentín Q Sierra

  • Roy Adams

  • Francene Larzelere Sinquah

  • Paul Rebman

  • Jacob L Taylor

  • May 11, 2026

  • 0 min

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  • 1

    Suicide rates among American Indian and Alaska Native individuals are the highest in the U.S., with a rate of 23.8 per 100,000 in 2023.

  • 2

    Existing suicide risk screening tools are insufficiently validated for American Indian and Alaska Native populations, necessitating tailored approaches.

  • 3

    Machine learning models using electronic health record data show promise in identifying suicide risk but face challenges with false positives.

  • 4

    The study evaluates the integration of a machine learning model with the ASQ screening tool to improve suicide risk identification in emergency departments.

  • 5

    Parallel and serial testing strategies were hypothesized to enhance case detection and reduce false positives in suicide risk assessment.

Original Source(s)

Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study

  • by Novalene Alsenay Goklish, Emily E Haroz, Rohan R Dayal, Valentín Q Sierra, Roy Adams, Francene Larzelere Sinquah, Paul Rebman, Jacob L Taylor

  • May 11, 2026

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