NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
This work addresses the complex process of clinical information recognition and normalization for healthcare applications, but it is incremental as it applies neural sequence labeling and parsing to a specific shared task.
The paper tackled the problem of extracting, normalizing, and ranking ICD codes from Spanish electronic health records for tumor morphology mentions, achieving F1 scores of 85.3, 76.7, and 77.0 MAP across three tasks.
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, we describe our system for the CANTEMIST shared task, which is able to extract, normalize and rank ICD codes from Spanish electronic health records using neural sequence labeling and parsing approaches with context-aware embeddings. Our best system achieves 85.3 F1, 76.7 F1, and 77.0 MAP for the three tasks, respectively.