KELSA: A Knowledge-Enriched Local Sequence Alignment Algorithm for Comparing Patient Medical Records

Ming Huang, Nilay D. Shah, Lixia Yao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sequence alignment methods have the promise to reserve important temporal information in electronic health records (EHRs) for comparing patient medical records. Compared to global sequence alignment, local sequence alignment is more useful when comparing patient medical records. One commonly used local sequence alignment algorithm is Smith-Waterman algorithm (SWA), which is widely used for aligning biological sequence. However directly applying this algorithm to align patient medical records will obtain suboptimal performance since it fails to consider complex situations in EHRs such as the temporality of medical events. In this work, we propose a new algorithm called Knowledge-Enriched Local Sequence Alignment algorithm (KELSA), which incorporates meaningful medical knowledge during sequence alignments. We evaluate our algorithm by comparing it to SWA on synthetic EHR data where the reference alignments are known. Our results show that KELSA aligns better than SWA by inserting new daily events and identifying more similarities between patient medical records. Compared to SWA, KELSA is more suitable for locally comparing patient medical records.

Original languageEnglish (US)
Title of host publicationExplainable AI in Healthcare and Medicine - Building a Culture of Transparency and Accountability
EditorsArash Shaban-Nejad, Martin Michalowski, David L. Buckeridge
PublisherSpringer Science and Business Media Deutschland GmbH
Pages227-240
Number of pages14
ISBN (Print)9783030533519
DOIs
StatePublished - 2021
EventAAAI International Workshop on Health Intelligence, W3PHIAI 2020 - New York City, United States
Duration: Feb 7 2020Feb 7 2020

Publication series

NameStudies in Computational Intelligence
Volume914
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

ConferenceAAAI International Workshop on Health Intelligence, W3PHIAI 2020
CountryUnited States
CityNew York City
Period2/7/202/7/20

ASJC Scopus subject areas

  • Artificial Intelligence

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