Hyperthyroidism progress prediction with enhanced LSTM

Haiqin Lu, Mei Wang, Weiliang Zhao*, Tingwei Su, Jian Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

In this work, we propose a method to predict the progress of the hyperthyroidism disease based on the sequence of the patient’s blood test data in the early stage. Long-Short-Term-Memory (LSTM) network is employed to process the sequence information in the tests. We design an adaptive loss function for the LSTM learning. We set bigger weights to the blood test data samples which are nearby the range boundaries when judging the hyperthyroidism. We have carried out a set of experiments against a real world dataset from a hospital in Shanghai, China. The experimental results show that our method outperforms the traditional LSTM significantly.

Original languageEnglish
Title of host publicationWeb and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
EditorsXin Wang, Rui Zhang, Young-Koo Lee, Le Sun, Yang-Sae Moon
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages209-217
Number of pages9
ISBN (Electronic)9783030602901
ISBN (Print)9783030602895
DOIs
Publication statusPublished - 2020
Event4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 - Tianjin, China
Duration: 18 Sep 202020 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12318 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020
CountryChina
CityTianjin
Period18/09/2020/09/20

Keywords

  • Hyperthyroidism
  • LSTM
  • Progress prediction
  • Adaptive loss function

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