@inproceedings{2f3d2124322648fd97aa9c116af22515,
title = "Hyperthyroidism progress prediction with enhanced LSTM",
abstract = "In this work, we propose a method to predict the progress of the hyperthyroidism disease based on the sequence of the patient{\textquoteright}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.",
keywords = "Hyperthyroidism, LSTM, Progress prediction, Adaptive loss function",
author = "Haiqin Lu and Mei Wang and Weiliang Zhao and Tingwei Su and Jian Yang",
year = "2020",
doi = "10.1007/978-3-030-60290-1_16",
language = "English",
isbn = "9783030602895",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "209--217",
editor = "Xin Wang and Rui Zhang and Young-Koo Lee and Le Sun and Yang-Sae Moon",
booktitle = "Web and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings",
address = "United States",
note = "4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 ; Conference date: 18-09-2020 Through 20-09-2020",
}