Evaluating global and local sequence alignment methods for comparing patient medical records

Abstract

BackgroundSequence alignment is a way of arranging sequences (e.g., DNA, RNA, protein, natural language, financial data, or medical events) to identify the relatedness between two or more sequences and regions of similarity. For Electronic Health Records (EHR) data, sequence alignment helps to identify patients of similar disease trajectory for more relevant and precise prognosis, diagnosis and treatment of patients.MethodsWe tested two cutting-edge global sequence alignment methods, namely dynamic time warping (DTW) and Needleman-Wunsch algorithm (NWA), together with their local modifications, DTW for Local alignment (DTWL) and Smith-Waterman algorithm (SWA), for aligning patient medical records. We also used 4 sets of synthetic patient medical records generated from a large real-world EHR database as gold standard data, to objectively evaluate these sequence alignment algorithms.ResultsFor global sequence alignments, 47 out of 80 DTW alignments and 11 out of 80 NWA alignments had superior similarity scores than reference alignments while the rest 33 DTW alignments and 69 NWA alignments had the same similarity scores as reference alignments. Forty-six out of 80 DTW alignments had better similarity scores than NWA alignments with the rest 34 cases having the equal similarity scores from both algorithms. For local sequence alignments, 70 out of 80 DTWL alignments and 68 out of 80 SWA alignments had larger coverage and higher similarity scores than reference alignments while the rest DTWL alignments and SWA alignments received the same coverage and similarity scores as reference alignments. Six out of 80 DTWL alignments showed larger coverage and higher similarity scores than SWA alignments. Thirty DTWL alignments had the equal coverage but better similarity scores than SWA. DTWL and SWA received the equal coverage and similarity scores for the rest 44 cases.ConclusionsDTW, NWA, DTWL and SWA outperformed the reference alignments. DTW (or DTWL) seems to align better than NWA (or SWA) by inserting new daily events and identifying more similarities between patient medical records. The evaluation results could provide valuable information on the strengths and weakness of these sequence alignment methods for future development of sequence alignment methods and patient similarity-based studies.