Cross-narrative Temporal Ordering of Medical Events

Abstract

Cross-narrative temporal ordering of medical events is essential to the task of generating a comprehensive timeline over a patient's history. We address the problem of aligning multiple medical event sequences, corresponding to different clinical narratives, comparing the following approaches: (1) A novel weighted finite state transducer representation of medical event sequences that enables composition and search for decoding, and (2) Dynamic programming with iterative pairwise alignment of multiple sequences using global and local alignment algorithms. The cross-narrative coreference and temporal relation weights used in both these approaches are learned from a corpus of clinical narratives. We present results using both approaches and observe that the finite state transducer approach performs performs significantly better than the dynamic programming one by 6.8% for the problem of multiple-sequence alignment.