TY - GEN
T1 - TRACE
T2 - VLDB workshops: International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021 and 7th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2021
AU - Wang, Yu
AU - Guan, Ziqiao
AU - Hou, Wei
AU - Wang, Fusheng
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and comorbidities associated with it. The early detection of CKD faces challenges of insufficient medical histories of positive patients and complicated risk factors. In this paper, we propose the TRACE (Transformer-RNN Autoencoder-enhanced CKD Detector) framework, an end-to-end prediction model using patients’ medical history data, to deal with these challenges. TRACE presents a comprehensive medical history representation with a novel key component: a Transformer-RNN autoencoder. The autoencoder jointly learns a medical concept embedding via Transformer for each hospital visit, and a latent representation which summarizes a patient’s medical history across all the visits. We compared TRACE with multiple state-of-the-art methods on a dataset derived from real-world medical records. Our model has achieved 0.5708 AUPRC with a 2.31% relative improvement over the best-performing method. We also validated the clinical meaning of the learned embeddings through visualizations and a case study, showing the potential of TRACE to serve as a general disease prediction model.
AB - Chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and comorbidities associated with it. The early detection of CKD faces challenges of insufficient medical histories of positive patients and complicated risk factors. In this paper, we propose the TRACE (Transformer-RNN Autoencoder-enhanced CKD Detector) framework, an end-to-end prediction model using patients’ medical history data, to deal with these challenges. TRACE presents a comprehensive medical history representation with a novel key component: a Transformer-RNN autoencoder. The autoencoder jointly learns a medical concept embedding via Transformer for each hospital visit, and a latent representation which summarizes a patient’s medical history across all the visits. We compared TRACE with multiple state-of-the-art methods on a dataset derived from real-world medical records. Our model has achieved 0.5708 AUPRC with a 2.31% relative improvement over the best-performing method. We also validated the clinical meaning of the learned embeddings through visualizations and a case study, showing the potential of TRACE to serve as a general disease prediction model.
KW - Chronic kidney disease prediction
KW - Deep learning
KW - Electronic health records
KW - Transformer
UR - https://www.scopus.com/pages/publications/85122592432
U2 - 10.1007/978-3-030-93663-1_13
DO - 10.1007/978-3-030-93663-1_13
M3 - Conference contribution
AN - SCOPUS:85122592432
SN - 9783030936624
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 182
BT - Heterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2021 and DMAH 2021, Revised Selected Papers
A2 - Rezig, El Kindi
A2 - Gadepally, Vijay
A2 - Mattson, Timothy
A2 - Stonebraker, Michael
A2 - Kraska, Tim
A2 - Wang, Fusheng
A2 - Luo, Gang
A2 - Kong, Jun
A2 - Dubovitskaya, Alevtina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 20 August 2021 through 20 August 2021
ER -