Study. Face-to-face examination combined with machine learning model is best for predicting suicide risk

The combined approach, using both face-to-face demonstrations and the EHR built-in machine learning model, has done its best in predicting adult suicide risk. The study is published Open JAMA network.

The study included more than 120,000 appointments with more than 83,000 patients in inpatient, outpatient, and emergency departments. It found that the hybrid approach using the “individual screening with the Columbia Suicide Severity Scale (C-SSRS)” with the “Vanderbilt Suicide Attempt Probability” (VSAIL) machine learning model was superior to any single option , when it came to predicting suicide attempts գաղափար suicide ideas.

“These findings suggest that health care systems should seek to use the traditional strengths of traditional clinical assessment, independent of automated machine learning, to further enhance suicide risk detection,” the study authors wrote.

WHY IS IT IMPORTANT?

The researchers note that the hybrid approach could have worked better for predicting suicide risk as it combined two models with additional strengths and weaknesses.

For example, the VSAIL model performed better at lower suicide risk thresholds, while the C-SSRS face-to-face examination performed better at higher risk thresholds. The sensitivity of the personal query has also decreased over time, while the VSAIL model has increased. The hybrid approach has shown backward performance over time.

At the same time, C-SSRS screening may be restricted to patients who deny suicide, even if it exists, while the VSAIL machine learning model may be less effective if the patient does not have extensive clinical data available.

“Our results suggest that EHR-based models should include available individual screening data to improve և PPV sensitivity. [positive predictive value] “(especially in the case of higher risk thresholds),” the researchers wrote.

“For most healthcare systems that perform only face-to-face screening, the inclusion of EHR-based models can improve sensitivity to low-risk thresholds, provide consistent results in reducing more specific decisions, and identify cases that are usually overlooked in clinical evaluation. (for example, cases of non-disclosure of the patient).

BIGGER TREND

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However, the COVID-19 epidemic intensified Mental health concerns around the worldշատ Many US states face a shortage of suppliers.

It: Open JAMA network The authors of the study noted that while it takes time to create and validate a machine learning model, individual examinations also require time, training, and practical mental health resources.

“Improving the integration of personal screening and historical EHR data (especially in PPV) has been clinically significant, and the costs and benefits of our ensemble approach will vary widely across healthcare settings,” they wrote. “Further research is needed to compare alternative ways of combining clinical-statistical risk predictions to analyze the practical implications of their use in clinical systems.”

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