Name
Abstract | ||
|---|---|---|
Objective: Neural network deidentification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world situations, however, researchers often have limited or no in-house training data. Existing systems and external data can help jump-start deidentification on in-house data; however, the most efficient way of utilizing existing systems and external data is unclear. This article investigates the transferability of a state-of-the-art neural clinical deidentification system, NeuroNER, across a variety of datasets, when it is modified architecturally for domain generalization and when it is trained strategically for domain transfer. Materials and Methods: We conducted a comparative study of the transferability of NeuroNER using 4 clinical note corpora with multiple note types from 2 institutions. We modified NeuroNER architecturally to integrate 2 types of domain generalization approaches. We evaluated each architecture using 3 training strategies. We measured transferability from external sources; transferability across note types; the contribution of external source data when in-domain training data are available; and transferability across institutions. Results and Conclusions: Transferability from a single external source gave inconsistent results. Using additional external sources consistently yielded an F-1-score of approximately 80%. Fine-tuning emerged as a dominant transfer strategy, with or without domain generalization. We also found that external sources were useful even in cases where in-domain training data were available. Transferability across institutions differed by note type and annotation label but resulted in improved performance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/jamia/ocab207 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | DocType | Volume |
deidentification, generalizability, transferability, domain generalization | Journal | 28 |
Issue | ISSN | Citations |
12 | 1067-5027 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kahyun Lee | 1 | 0 | 0.34 |
| Nicholas J Dobbins | 2 | 0 | 0.34 |
| Bridget McInnes | 3 | 0 | 0.34 |
| Meliha Yetisgen-Yildiz | 4 | 328 | 34.25 |
| Özlem Uzuner | 5 | 1045 | 67.09 |