A cross-institutional evaluation on breast cancer phenotyping NLP algorithms on electronic health records

Abstract

Objective: Transformer-based language models are prevailing in the clinical domain due to their excellent performance on clinical NLP tasks. The generalizability of those models is usually ignored during the model development process. This study evaluated the generalizability of CancerBERT, a Transformer-based clinical NLP model, along with classic machine learning models, i.e., conditional random field (CRF), bi-directional long short-term memory CRF (BiLSTM-CRF), across different clinical institutes through a breast cancer phenotype extraction task.

Materials and methods: Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota (UMN) and Mayo Clinic (MC), and annotated following the same guideline. We developed three types of NLP models (i.e., CRF, BiLSTM-CRF and CancerBERT) to extract cancer phenotypes from clinical texts. We evaluated the generalizability of models on different test sets with different learning strategies (model transfer vs locally trained). The entity coverage score was assessed with their association with the model performances.

Results: We manually annotated 200 and 161 clinical documents at UMN and MC. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932).

Conclusions: The results indicate the CancerBERT model has superior learning ability and generalizability among the three types of clinical NLP models for our named entity recognition task. It has the advantage to recognize complex entities, e.g., entities with different labels.

Keywords: Electronic health records; Generalizability; Information extraction; Natural language processing.

Graphical abstract

Fig. 1

The pipeline of the study. Data were collected and annotated from the UMN and MC. The UMN models were externally evaluated on MC data. UMN models were further refined on MC data and evaluated as comparisons. Permutation dataset evaluation and Entity coverage ratio analysis were conducted to explore the model generalizability.

Fig. 2

The performances (strict F1 scores) of CRF_UMN, BiLSTM-CRF_UMN, and CancerBERT_{UMN_397} models on different test sets. The original test set is the UMN test set, and the portability test set is MC test set. All models were UMN models trained solely on UMN data.

Fig. 3

The performances (strict F1 scores) of CRF_UMN, BiLSTM-CRF_UMN, and CancerBERT_{UMN_397} models for the identification of entities in different ECR groups. Group 1: 0 < = ECR < 0.33, Group 2: 0.33 < = ECR < 0.67, Group 3: 0.67 < = ECR< 1, and Group 4: ECR = 1.