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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 10
2961-2965,2970

Research on intelligent diagnosis sequence prediction based on FT-LSTM model

Wang Lutong1,2
Wang Hong1,2
Song Yongqiang1,2
Wang Qian1,2
1. School of Information Science & Engineering, Shandong Normal University, Jinan 250358, China
2. Shandong Provincial Key Laboratory of Distributed Computing Software, Jinan 250358, China
DOI: 10.19734/j.issn.1001-3695.2019.04.0192

Abstract

Aiming at the problems of time series data extraction and medical concept expression difficulty in electronic medical record(EMR) field, this paper proposed a time-controlled long-term and short-term memory neural network(FT-LSTM) prediction model. The model efficiently captured multidimensional features, and further enabled accurate prediction of future clinical events. First, it used the FastText method to interpret vector representations of medical events, more effectively capture rich conceptual relationships in medical information. Then, it designed a time gate based on the original LSTM model to better capture the long-term and short-term information, model the event information to improve the prediction performance. The experimental results on the MIMIC-Ⅲ dataset show that, using the FT-LSTM model can predict event results with higher precision, which is of great significance.

Foundation Support

国家自然科学基金资助项目(61672329,61373149,61472233,61572300,81273704)
山东省科技计划资助项目(2014GGX101026)
山东省教育科学规划资助项目(ZK1437B010)
山东省泰山学者基金资助项目(TSHW201502038,20110819)
山东省精品课程资助项目 (2012BK294,2013BK399,2013BK402)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.04.0192
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 10
Section: Algorithm Research & Explore
Pages: 2961-2965,2970
Serial Number: 1001-3695(2020)10-016-2961-05

Publish History

[2020-10-05] Printed Article

Cite This Article

王露潼, 王红, 宋永强, 等. 基于FT-LSTM模型的临床事件诊断序列预测研究 [J]. 计算机应用研究, 2020, 37 (10): 2961-2965,2970. (Wang Lutong, Wang Hong, Song Yongqiang, et al. Research on intelligent diagnosis sequence prediction based on FT-LSTM model [J]. Application Research of Computers, 2020, 37 (10): 2961-2965,2970. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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