@inproceedings{64c2f0f2ba3045698761de4a631e3246,
title = "Data-driven generation of medical-research hypotheses in cancer patients",
abstract = "Hypotheses are the most important part of medical research. If we have a good hypothesis, we can design experiments and verify it. Therefore, we use the associations generated by association rule as hypotheses in clinical medicine research. We hope this method can help physicians quickly and correctly find research hypotheses. This experiment was divided into two parts. In the first part, we used the Apriori algorithm to find associations between cancer and other catastrophic illnesses. In the second part, we used these associations as medical-research hypotheses and designed cohort studies to verify them. In this study, we proved that the association-rules method could help clinical physicians quickly and correctly obtain clinical-medicine hypotheses.",
author = "Chang, {Hsin Hsiung} and Chiang, {Jung Hsien} and Chu, {Cheng Chung}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
month = jul,
doi = "10.1109/COMPSAC51774.2021.00091",
language = "English",
series = "Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "626--631",
editor = "Chan, {W. K.} and Bill Claycomb and Hiroki Takakura and Ji-Jiang Yang and Yuuichi Teranishi and Dave Towey and Sergio Segura and Hossain Shahriar and Sorel Reisman and Ahamed, {Sheikh Iqbal}",
booktitle = "Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",
address = "United States",
}