Discovering Disease Patterns Using the Supervised Topic Model

Shu Li; Jingyuan Wang; Yi Wang

doi:10.1109/ICSSSM.2018.8465101

Discovering Disease Patterns Using the Supervised Topic Model

Shu Li
, Jingyuan Wang^*
, Yi Wang

^*Corresponding author for this work

School of Computer Science and Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we explore the methods of medical data mining. The medical data usually have some unique characteristics such as sparseness, highly correlated features and unbalanced sample categories. After researching the models commonly used in current medical data mining, we use the topic-based model for medical data mining. We build a supervised topic model (the SLDA model) and use Gibbs sampling to estimate parameters. From the results of the model, we can find some important relationships among features in our medical data. Finally, the SLDA model was combined with a Random Forest classifier, which gets good predictive performance in disease prediction.

Original language	English
Title of host publication	2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538651780
DOIs	https://doi.org/10.1109/ICSSSM.2018.8465101
State	Published - 13 Sep 2018
Event	15th International Conference on Service Systems and Service Management, ICSSSM 2018 - Hangzhou, China Duration: 21 Jul 2018 → 22 Jul 2018

Publication series

Name	2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018

Conference

Conference	15th International Conference on Service Systems and Service Management, ICSSSM 2018
Country/Territory	China
City	Hangzhou
Period	21/07/18 → 22/07/18

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10.1109/ICSSSM.2018.8465101

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Li, S., Wang, J., & Wang, Y. (2018). Discovering Disease Patterns Using the Supervised Topic Model. In 2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018 Article 8465101 (2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSSSM.2018.8465101

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title = "Discovering Disease Patterns Using the Supervised Topic Model",

abstract = "In this paper, we explore the methods of medical data mining. The medical data usually have some unique characteristics such as sparseness, highly correlated features and unbalanced sample categories. After researching the models commonly used in current medical data mining, we use the topic-based model for medical data mining. We build a supervised topic model (the SLDA model) and use Gibbs sampling to estimate parameters. From the results of the model, we can find some important relationships among features in our medical data. Finally, the SLDA model was combined with a Random Forest classifier, which gets good predictive performance in disease prediction.",

author = "Shu Li and Jingyuan Wang and Yi Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th International Conference on Service Systems and Service Management, ICSSSM 2018 ; Conference date: 21-07-2018 Through 22-07-2018",

year = "2018",

month = sep,

day = "13",

doi = "10.1109/ICSSSM.2018.8465101",

language = "英语",

isbn = "9781538651780",

series = "2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018",

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}

Li, S, Wang, J & Wang, Y 2018, Discovering Disease Patterns Using the Supervised Topic Model. in 2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018., 8465101, 2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018, Institute of Electrical and Electronics Engineers Inc., 15th International Conference on Service Systems and Service Management, ICSSSM 2018, Hangzhou, China, 21/07/18. https://doi.org/10.1109/ICSSSM.2018.8465101

Discovering Disease Patterns Using the Supervised Topic Model. / Li, Shu; Wang, Jingyuan; Wang, Yi.
2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8465101 (2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Jingyuan

AU - Wang, Yi

PY - 2018/9/13

Y1 - 2018/9/13

N2 - In this paper, we explore the methods of medical data mining. The medical data usually have some unique characteristics such as sparseness, highly correlated features and unbalanced sample categories. After researching the models commonly used in current medical data mining, we use the topic-based model for medical data mining. We build a supervised topic model (the SLDA model) and use Gibbs sampling to estimate parameters. From the results of the model, we can find some important relationships among features in our medical data. Finally, the SLDA model was combined with a Random Forest classifier, which gets good predictive performance in disease prediction.

AB - In this paper, we explore the methods of medical data mining. The medical data usually have some unique characteristics such as sparseness, highly correlated features and unbalanced sample categories. After researching the models commonly used in current medical data mining, we use the topic-based model for medical data mining. We build a supervised topic model (the SLDA model) and use Gibbs sampling to estimate parameters. From the results of the model, we can find some important relationships among features in our medical data. Finally, the SLDA model was combined with a Random Forest classifier, which gets good predictive performance in disease prediction.

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DO - 10.1109/ICSSSM.2018.8465101

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SN - 9781538651780

T3 - 2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018

BT - 2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on Service Systems and Service Management, ICSSSM 2018

Y2 - 21 July 2018 through 22 July 2018

ER -

Li S, Wang J, Wang Y. Discovering Disease Patterns Using the Supervised Topic Model. In 2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8465101. (2018 15th International Conference on Service Systems and Service Management, ICSSSM 2018). doi: 10.1109/ICSSSM.2018.8465101

Discovering Disease Patterns Using the Supervised Topic Model

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