Data Analytics with a Purpose

Improving Mental Health through Machine Learning

The Walsh Lab translates Healthcare Data to Action through Machine Learning and Decision Support for vulnerable populations like those with suicidality and mental illness.

Our research focuses on the application of data science to complex healthcare data to enable mental, behavioral, and population health. Our collaborators are internists, psychiatrists, psychologists, engineers, epidemiologists, statisticians, community case managers, mobile crisis teams, and more.

We translate our predictive algorithms through informatics and implementation science to the point-of-care. We have partnered with other research teams around the world to glean insights from these complex data to predict and prevent self-harm and to improve mental health.

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Media

What’s Next? New Pathways For Suicide Prevention

September 25, 2022

Hear the Episode

 

Vanderbilt Discover DNA Podcast

Opioid overdoses more readily preventable with ensemble learning

October 28, 2021

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AI in Healthcare

The Undiscovered Country – Can Suicide Be Predicted? 

August 2021

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Harper’s Magazine

Screening Algorithm Flags Patients At Risk of Suicide

March 22, 2021

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Diagnostics World News

Artificial Intelligence System Calculates Suicide Attempt Risk – Here’s How It Performed

March 12, 2021

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SciTechDaily

    Stay up to date with the latest developments in our work in The Walsh Lab.
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    Dr. Walsh Inducted into the American College of Medical Informatics

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    New Paper – Improving ascertainment of suicidal ideation and suicide attempt with natural language processing

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    Dr. Walsh a Panelist for NASEM’s Workshop on Innovative Data Science Approaches to Assess Suicide Risk in Individuals, Populations, and Communities

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    Dr. Walsh Named a 2022 Chancellor Faculty Fellow at Vanderbilt

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    New Paper – Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among Adults

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    New Paper – Risky business: a scoping review for communicating results of predictive models between providers and patients

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