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A method combining LDA and neural networks for antitumor drug efficacy prediction

文献类型：期刊论文

作者Zhu, Weiwei1,2; Zhang, Lei3; Jiang, Xiaodong4; Zhou, Peng5; Xie, Xinping6; Wang, Hongqiang2
刊名DIGITAL HEALTH
出版日期2024
卷号10
关键词Drug efficacy prediction text mining LDA model neural network clinical big data
ISSN号2055-2076
DOI10.1177/20552076241280103
通讯作者Xie, Xinping(592745039@qq.com) ; Wang, Hongqiang(hqwang126@126.com)
英文摘要Background Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task.Objective This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data.Methods This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC).Results The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice.Conclusions Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.
WOS关键词HYPERPARAMETERS
资助项目University Science Research Project of the Education Department of Anhui Province ; Anhui Province's key Research and Development Project ; National Natural Science Foundation of China ; Laboratory of Operations Research and Data Science of Anhui Jianzhu University ; Introduction of high-level talent research funding projects of Hefei Normal University[KJ2021A0633] ; Introduction of high-level talent research funding projects of Hefei Normal University[201904a07020092] ; Introduction of high-level talent research funding projects of Hefei Normal University[81872276] ; Introduction of high-level talent research funding projects of Hefei Normal University[YCSJ2024ZR02] ; Introduction of high-level talent research funding projects of Hefei Normal University[61973295] ; Introduction of high-level talent research funding projects of Hefei Normal University[60423018]
WOS研究方向Health Care Sciences & Services ; Public, Environmental & Occupational Health ; Medical Informatics
语种英语
WOS记录号WOS:001308718200001
出版者SAGE PUBLICATIONS LTD
资助机构University Science Research Project of the Education Department of Anhui Province ; Anhui Province's key Research and Development Project ; National Natural Science Foundation of China ; Laboratory of Operations Research and Data Science of Anhui Jianzhu University ; Introduction of high-level talent research funding projects of Hefei Normal University
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/135073]  
专题中国科学院合肥物质科学研究院
通讯作者Xie, Xinping; Wang, Hongqiang
作者单位1.Univ Sci & Technol China, Hefei, Anhui, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
3.Univ Sci & Technol China, Affiliated Hosp USTC 1, Dept Pharm, Div Life Sci & Med, Hefei, Anhui, Peoples R China
4.Univ Sci & Technol China, Affiliated Hosp 1, Med Oncol Dept, Hefei, Anhui, Peoples R China
5.Hefei Normal Univ, Sch Life Sci, Hefei, Anhui, Peoples R China
6.Anhui Jianzhu Univ, Sch Math & Phys, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Weiwei,Zhang, Lei,Jiang, Xiaodong,et al. A method combining LDA and neural networks for antitumor drug efficacy prediction[J]. DIGITAL HEALTH,2024,10.
APA Zhu, Weiwei,Zhang, Lei,Jiang, Xiaodong,Zhou, Peng,Xie, Xinping,&Wang, Hongqiang.(2024).A method combining LDA and neural networks for antitumor drug efficacy prediction.DIGITAL HEALTH,10.
MLA Zhu, Weiwei,et al."A method combining LDA and neural networks for antitumor drug efficacy prediction".DIGITAL HEALTH 10(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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