Using transfer learning to improve prediction of suicide risk in acute care hospitals.

Objective: Emerging efforts to identify patients at risk of suicide have focused on the development of predictive algorithms for use in healthcare settings. We address a major challenge in effective risk modeling in healthcare settings with insufficient data with which to create and apply risk models. This study aimed to improve risk prediction using transfer learning or data fusion by incorporating risk information from external data sources to augment the data available in particular clinical settings.

Materials and methods: In this retrospective study, we developed predictive models in individual Connecticut hospitals using medical claims data. We compared conventional models containing demographics and historical medical diagnosis codes with fusion models containing conventional features and fused risk information that described similarities in historical diagnosis codes between patients from the hospital and patients receiving care for suicide attempts at other hospitals.

Results: Our sample contained 27 hospitals and 636 758 18- to 64-year-old patients. Fusion improved prediction for 93% of hospitals, while slightly worsening prediction for 7%. Median areas under the ROC and precision-recall curves of conventional models were 77.6% and 3.4%, respectively. Fusion improved these metrics by a median of 3.3 and 0.3 points, respectively (Ps < .001). Median sensitivities and positive predictive values at 90% and 95% specificity were also improved (Ps < .001).

Discussion: This study provided strong evidence that data fusion improved model performance across hospitals. Improvement was of greatest magnitude in facilities treating relatively few suicidal patients.

Conclusion: Data fusion holds promise as a methodology to improve suicide risk prediction in healthcare settings with limited or incomplete data.