A likelihood-based convolution approach to estimate major health events in longitudinal health records data: an external validation study

Objective: In electronic health record data, the exact time stamp of major health events, defined by significant physiologic or treatment changes, is often missing. We developed and externally validated a method that can accurately estimate these time stamps based on accurate time stamps of related data elements. Materials and Methods: A novel convolution-based change detection methodology was developed and tested using data from the national deidentified clinical claims OptumLabs data warehouse, then externally validated on a single center dataset derived from the M Health Fairview system. Results: We applied the methodology to estimate time to liver transplantation for waitlisted candidates. The median error between estimated date within the period of the actual true date was zero days, and median error was 92% and 84% of the transplants, in development and validation samples, respectively. Discussion: The proposed method can accurately estimate missing time stamps. Successful external validation suggests that the proposed method does not need to be refit to each health system; thus, it can be applied even when training data at the health system is insufficient or unavailable. The proposed method was applied to liver transplantation but can be more generally applied to any missing event that is accompanied by multiple related events that have accurate time stamps. Conclusion: Missing time stamps in electronic healthcare record data can be estimated using time stamps of related events. Since the model was developed on a nationally representative dataset, it could be successfully transferred to a local health system without substantial loss of accuracy.

Automated summary

A novel convolution-based change detection methodology was developed and successfully validated on different datasets, accurately estimating missing time stamps in electronic health record data. The method showed high estimation accuracy in events like liver transplantation and demonstrated good generalizability.