Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data

Objective Reducing suicidal behavior among patients in the healthcare system requires accurate and explainable predictive models of suicide risk across diverse healthcare settings. Materials and Methods We proposed a general targeted fusion learning framework that can be used to build a tailored risk prediction model for any specific healthcare setting, drawing on information fusion from a separate more comprehensive dataset with indirect sample linkage through patient similarities. As a proof of concept, we predicted suicide-related hospitalizations for pediatric patients in a limited statewide Hospital Inpatient Discharge Dataset (HIDD) fused with a more comprehensive medical All-Payer Claims Database (APCD) from Connecticut. Results We built a suicide risk prediction model for the source data (APCD) and calculated patient risk scores. Patient similarity scores between patients in the source and target (HIDD) datasets using their demographic characteristics and diagnosis codes were assessed. A fused risk score was generated for each patient in the target dataset using our proposed targeted fusion framework. With this model, the averaged sensitivities at 90% and 95% specificity improved by 67% and 171%, and the positive predictive values for the combined fusion model improved 64% and 135% compared to the conventional model. Discussion and Conclusions We proposed a general targeted fusion learning framework that can be used to build a tailored predictive model for any specific healthcare setting. Results from this study suggest we can improve the performance of predictive models in specific target settings without complete integration of the raw records from external data sources.

Automated summary

To reduce suicidal behavior among patients in the healthcare system, accurate and explainable predictive models of suicide risk are required. A general targeted fusion learning framework was proposed to build tailored risk prediction models for specific healthcare settings, using information fusion from a more comprehensive dataset. Results suggest that performance of predictive models in specific target settings can be improved without complete integration of raw records from external data sources.