Mr. Jian Wang Profile

Jian Wang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 April 2023 Paper

Second order relation graph embedding neural network for rational drug use prediction

Zhonghui Deng, Hai Yuan, Wei Feng, Zheng Xu, Jian Wang, Weijun Wang

Proceedings Volume 12602, 1260230 (2023) https://doi.org/10.1117/12.2668194

KEYWORDS: Performance modeling, Neural networks, Surgery, Data modeling, Systems modeling, Tunable filters, Frequency modulation, Fermium, Education and training, Technology

Read Abstract +

Rational drug use prediction, which is used to estimate whether the drug use is reasonable in clinical medical treatment, is of great significance in combating excessive medical treatment. The feature interaction model based on electronic medical records is widely used in the field of medical behavior prediction. However, the features recorded in electronic medical records are very complex, and medical behavior has an obvious long-tail effect. The problem of feature sparseness makes traditional prediction models based on feature interaction impossible. It works well, we propose a novel business domain-based second-order relational graph embedding neural network model (SORGE-NN), which can distinguish the scope of features and mine the implicit propagation relationship, through the residual-multi-head attention layer , and perform high-order weighted combination of features, which effectively alleviates the challenges brought by feature sparse and complex features. We conduct experiments with real datasets, and the experimental results show that our proposed SORGE-NN achieves better results than current state-of-the-art prediction models.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years