可追溯性
计算机科学
推论
公制(单位)
水准点(测量)
人工智能
机器学习
一般化
数据挖掘
软件工程
数学
大地测量学
运营管理
数学分析
经济
地理
作者
Jianing Xi,Dan Wang,Xuebing Yang,Wensheng Zhang,Qinghua Huang
标识
DOI:10.1016/j.bspc.2022.104144
摘要
The application of Artificial Intelligence (AI) on cancer drug recommendation can prompt the development of personalized cancer therapy. However, most of the current AI drug recommendations cannot give explainable inferences, where their prediction procedures are black boxes, and are difficult to earn the trust of doctors or patients. In explainable inference, the key steps during the recommendation procedures can be located easily, facilitating model adjustment for wrong predictions and model generalization for new drugs/samples. In this paper, we analyze the necessity of developing explainable AI drug recommendation, and propose an evaluation metric called traceability rate. The traceability rate is calculated as the proportion of correct predictions that are traceable along the knowledge graph in all the ground truths. We further conduct an experiment on a benchmark drug response dataset to apply the traceability rate as evaluation metric, where the results show a trade-off between model performance and explainability. Therefore, the explainable AI drug recommendation still demands for further improvement to meet the requirement of clinical personalized therapy.
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