Sparse-attentive meta temporal point process for clinical decision support

Ru Y, Qiu X, Tan X, Chen B, Gao Y, Jin Y (2022)
Neurocomputing 485: 114-123.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Ru, Yajun; Qiu, Xihe; Tan, Xiaoyu; Chen, Bin; Gao, Yongbin; Jin, YaochuUniBi
Abstract / Bemerkung
In the study of clinical decision-making, prediction of future clinical events of patients has become an important task, especially for variant disease predictions. In previous studies, the disease prediction prob-lems are considered as binary classification based on the patients' electronic health records (EHRs), which lack the capacity to predict multiple types of diseases. In this paper, we propose a method which can pre-dict both the patients' disease types among various candidate diseases and patients' next hospital visit time. The next hospital visit time is crucial for medical experts in making decisions, because it reflects the onset time information of disease and provides sufficient information on the severity of the disease. Our proposed method is implemented based on the point process framework, which utilizes meta-learning to gain the prior knowledge of the individual patient's clinical data with context information, adopts sparse-attention to determine the importance of past major clinical events, and simulates the intensity of clinical events through Hawkes process to predict the types of diseases diagnosed by the doc-tor and patient' next hospital visit time. The experimental data are extracted from the public datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II) and Medical Information Mart for Intensive Care (MIMIC-III). Compared with the baseline time series models, our proposed method has achieved superior results, with a higher F1-score (66.67%) and a lower root-mean-square error (RMSE) (6.69) on the test set, which proves the effectiveness of the proposed method. We further study the self-attention mechanism based on Transformer and sparse-attention methods to demonstrate the valid-ity of our model. Our proposed method provides empirical evidence of its ability in facilitating the decision-making process of clinicians, which can be potentially utilized as effective clinical decision sup-port tools to better improve the quality of medical services and reduce medical errors.(c) 2022 Elsevier B.V. All rights reserved.
Stichworte
Meta-learning; Sparse-attention; Hawkes process; Clinical decision; support
Erscheinungsjahr
2022
Zeitschriftentitel
Neurocomputing
Band
485
Seite(n)
114-123
ISSN
0925-2312
eISSN
1872-8286
Page URI
https://pub.uni-bielefeld.de/record/2962699

Zitieren

Ru Y, Qiu X, Tan X, Chen B, Gao Y, Jin Y. Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing . 2022;485:114-123.
Ru, Y., Qiu, X., Tan, X., Chen, B., Gao, Y., & Jin, Y. (2022). Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing , 485, 114-123. https://doi.org/10.1016/j.neucom.2022.02.028
Ru, Y., Qiu, X., Tan, X., Chen, B., Gao, Y., and Jin, Y. (2022). Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing 485, 114-123.
Ru, Y., et al., 2022. Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing , 485, p 114-123.
Y. Ru, et al., “Sparse-attentive meta temporal point process for clinical decision support”, Neurocomputing , vol. 485, 2022, pp. 114-123.
Ru, Y., Qiu, X., Tan, X., Chen, B., Gao, Y., Jin, Y.: Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing . 485, 114-123 (2022).
Ru, Yajun, Qiu, Xihe, Tan, Xiaoyu, Chen, Bin, Gao, Yongbin, and Jin, Yaochu. “Sparse-attentive meta temporal point process for clinical decision support”. Neurocomputing 485 (2022): 114-123.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Suchen in

Google Scholar