New Study Finds Machine Learning Models Improve Identification of Children at Risk of Self-Harm. UCLA researchers developed effective methods to detect at-risk children. Read more.
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Current data storage and tracking methods fail to identify children at risk of self-harm, according to a study by UCLA Health. Machine learning models developed by researchers were more effective at detecting self-injurious thoughts or behaviors. This breakthrough highlights the limitations of current risk-prediction models, urging health systems to reevaluate their approach and leverage machine learning for improved detection. #YouthMentalHealth
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Explore the evolution of tech policy from Obama's optimism to Harris's vision at the Democratic National Convention. What's next for Democrats in tech?