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An Attentive LSTM based approach for adverse drug reactions prediction

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Abstract

Adverse drug reactions (ADRs), which are harmful physical reactions of patients to drug treatments, are inherent to the nature of drugs; the reactions can occur with any drug and are becoming a leading cause of patient morbidity and mortality during medical procedures. ADRs can be hazardous and even fatal to patients. In traditional methods, ADRs are detected through clinical trials. To obtain a comprehensive collection of ADRs, sufficient experimental samples and time are required before a drug comes to the market, which is not a realistic possibility. Moreover, even if extensive clinical trials are performed, many undetected ADRs might still be discovered after a drug is released to the market. ADRs can lead to disastrous consequences for humanity, which obviates a dramatically increased need for precise predictions of potential ADRs as early as possible. In this paper, we propose an encoder-decoder framework based on attention mechanism and the long short-term memory (LSTM) model to predict potential ADRs. We regard the prediction of ADRs as a sequence-to-sequence problem and improve the encoder-decoder framework based on the attention mechanism to learn the interrelationships between ADRs. Unlike other classical methods utilizing molecular drug structures, our model is based solely on ADRs, which is an independent but parallel approach compared to traditional methods. We capitalize on the mask method to generate the target data and use the 5-fold cross-validation method to cyclically verify the performance of our proposed model. Based on the Top-k accuracy test results, our model outperforms the baseline models in potential ADRs predictions.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No.62102241).

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Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No.333 Longteng Road, Shanghai, 201620, Shanghai, China

    Jiahui Qian, Xihe Qiu, Jue Chen & Xiaoyan Jiang

  2. Ant Group, No.566 Xixi Road, Hangzhou, 310013, Zhejiang, China

    Xiaoyu Tan

  3. School of Computer Science and Technology, Beijing Jiaotong University, No.3 ShangYuanCun, Haidian District, Beijing, 100044, Beijing, China

    Qiong Li

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  1. Jiahui Qian
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  2. Xihe Qiu
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Correspondence to Xihe Qiu.

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Jiahui Qian and Xihe Qiu contributed equally to this work

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Qian, J., Qiu, X., Tan, X. et al. An Attentive LSTM based approach for adverse drug reactions prediction. Appl Intell 53, 4875–4889 (2023). https://doi.org/10.1007/s10489-022-03721-y

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