TY - GEN
T1 - Causal Reasoning Methods in Medical Domain
T2 - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022
AU - Wu, Xing
AU - Li, Jingwen
AU - Qian, Quan
AU - Liu, Yue
AU - Guo, Yike
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (Grant No. 62172267), the Natural Science Foundation of Shanghai, China (Grant No. 20ZR1420400), the State Key Program of National Natural Science Foundation of China (Grant No. 61936001).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/8/29
Y1 - 2022/8/29
N2 - Causal reasoning has been a key topic in medical domain with many applications, in which the core problem is to infer the causal effects of medical treatments with data mining. However, there are obstacles such as unstable identification and false associations when applying traditional machine learning methods dealing with the effect estimation about medical treatments due to the large-scale and high-dimensionality of medical data. Furthermore, there is no thorough survey of causal reasoning methods for medical domain problems, which is an emerging research direction. To meet the challenge, the causal reasoning in medical domain is surveyed to systematically classify and summarize causal reasoning methods in two dimensions: four categories of core ideas and three levels of causal structure. The thorough review demonstrates that causal reasoning methods have theoretical and practical significance in medical domain, which is a research field full of potential.
AB - Causal reasoning has been a key topic in medical domain with many applications, in which the core problem is to infer the causal effects of medical treatments with data mining. However, there are obstacles such as unstable identification and false associations when applying traditional machine learning methods dealing with the effect estimation about medical treatments due to the large-scale and high-dimensionality of medical data. Furthermore, there is no thorough survey of causal reasoning methods for medical domain problems, which is an emerging research direction. To meet the challenge, the causal reasoning in medical domain is surveyed to systematically classify and summarize causal reasoning methods in two dimensions: four categories of core ideas and three levels of causal structure. The thorough review demonstrates that causal reasoning methods have theoretical and practical significance in medical domain, which is a research field full of potential.
KW - Automated reasoning
KW - Causal effect estimation
KW - Causal reasoning
KW - Causality
KW - Model-based reasoning
UR - http://www.scopus.com/inward/record.url?scp=85137999280&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08530-7_16
DO - 10.1007/978-3-031-08530-7_16
M3 - Conference proceeding
AN - SCOPUS:85137999280
SN - 9783031085291
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 196
BT - Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence
A2 - Fujita, Hamido
A2 - Fournier-Viger, Philippe
A2 - Ali, Moonis
A2 - Wang, Yinglin
PB - Springer Cham
Y2 - 19 July 2022 through 22 July 2022
ER -
