TY - GEN
T1 - KELSA
T2 - AAAI International Workshop on Health Intelligence, W3PHIAI 2020
AU - Huang, Ming
AU - Shah, Nilay D.
AU - Yao, Lixia
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-53352-6_21
DO - 10.1007/978-3-030-53352-6_21
M3 - Conference contribution
AN - SCOPUS:85097068497
SN - 9783030533519
T3 - Studies in Computational Intelligence
SP - 227
EP - 240
BT - Explainable AI in Healthcare and Medicine - Building a Culture of Transparency and Accountability
A2 - Shaban-Nejad, Arash
A2 - Michalowski, Martin
A2 - Buckeridge, David L.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 February 2020 through 7 February 2020
ER -