Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data.

Clicks: 328
ID: 70940
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #35 of 170 articles by views in Studies in health technology and informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 170 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.
Reference Key
wang2019developmentstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Yu-Hsiang;Nguyen, Phung-Anh;Islam, Md Mohaimenul;Li, Yu-Chuan;Yang, Hsuan-Chia;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190259
URL
Keywords
electronic health records colorectal neoplasms algorithms

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.