ICU Mortality Prediction Using Long Short-Term Memory Networks

被引：0

作者：

Mili, Manel ^{[1
,3
]}

Kerkeni, Asma ^{[2
,3
]}

Ben Abdallah, Asma ^{[2
,3
]}

Bedoui, Mohamed Hedi ^{[3
]}

机构：

[1] Univ Monastir, Fac Med, Monastir, Tunisia

[2] Univ Monastir, Higher Inst Comp Sci & Math, Monastir, Tunisia

[3] Univ Monastir, Lab Technol & Med Imaging, Fac Med, Monastir, Tunisia

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2022 | 2022年 / 13756卷

关键词：

Electronic health record; Multivariate time-series data; MIMIC-III;

D O I：

10.1007/978-3-031-21753-1_24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.

引用

页码：242 / 251

页数：10

共 50 条

[1] Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
Abbas, Zainab
Al-Shishtawy, Ahmad
Girdzijauskas, Sarunas
Vlassov, Vladimir
[J]. 2018 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS), 2018, : 57 - 65
[2] Using long short-term memory networks for river flow prediction
Xu, Wei
Jiang, Yanan
Zhang, Xiaoli
Li, Yi
Zhang, Run
Fu, Guangtao
[J]. HYDROLOGY RESEARCH, 2020, 51 (06): : 1358 - 1376
[3] Inspection route prediction in substation using long short-term memory networks
Yang, Yingyi
Yang, Fan
Wu, Hao
[J]. COMPUTERS & ELECTRICAL ENGINEERING, 2022, 98
[4] Tailings Pond Risk Prediction Using Long Short-Term Memory Networks
Li, Jianwei
Chen, Haoyu
Zhou, Ting
Li, Xiaowen
[J]. IEEE ACCESS, 2019, 7 : 182527 - 182537
[5] Aircraft Trajectory Prediction Using Deep Long Short-Term Memory Networks
Zhao, Ziyu
Zeng, Weili
Quan, Zhibin
Chen, Mengfei
Yang, Zhao
[J]. CICTP 2019: TRANSPORTATION IN CHINA-CONNECTING THE WORLD, 2019, : 124 - 135
[6] Location Prediction of Sperm Cells Using Long Short-Term Memory Networks
Noy, Lioz
Barnea, Itay
Dudaie, Matan
Kamber, Dotan
Levi, Mattan
Shaked, Natan T.
[J]. ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (09)
[7] Spectrum Usage Analysis And Prediction using Long Short-Term Memory Networks
Ghosh, Anneswa
Van der Merwe, Jacobus
Kasera, Sneha Kumar
[J]. PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING, ICDCN 2023, 2023, : 270 - 279
[8] Sepsis Deterioration Prediction Using Channelled Long Short-Term Memory Networks
Svenson, Peter
Haralabopoulos, Giannis
Torres, Mercedes Torres
[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE (AIME 2020), 2020, : 359 - 370
[9] Short-term Individual Electric Vehicle Charging Behavior Prediction Using Long Short-term Memory Networks
Khwaja, Ahmed S.
Venkatesh, Bala
Anpalagan, Alagan
[J]. 2020 IEEE 25TH INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS (CAMAD), 2020,
[10] ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE
Dogan, Erdem
[J]. SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT, 2020, 107 : 19 - 32

← 1 2 3 4 5 →