Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches

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Researchers develop machine learning models that could improve suicide-risk prediction among children

In addition, the findings suggest that although machine learning–based approaches to detection are potentially advantageous in improving sensitivity, it may not be necessary to have access to a highly comprehensive set of data elements to meaningfully improve the sensitivity of detection. Smaller sets of mental health-related data elements, both ICD-10-CM–code based and non–ICD-10-CM–code based, performed similarly to more comprehensive data elements in the detection task.

Chief complaints were documented through an electronic health record speed button and thus may not generalize to less-structured text descriptions of the presenting problem.Taken together, this study adds to existing efforts made toward developing clinical phenotypes of pediatric health conditions.

 

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