Hou Wen Juan, Ceesay Bamfa
1 Department of Computer Science and Information Engineering, National Taiwan Normal University, No 88, Tingzhou Road, Sec. 4, Taipei 116, Taiwan R.O.C.
J Bioinform Comput Biol. 2018 Dec;16(6):1840027. doi: 10.1142/S0219720018400279. Epub 2018 Oct 30.
Information on changes in a drug's effect when taken in combination with a second drug, known as drug-drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.
药物与另一种药物联合使用时其效果变化的信息，即药物相互作用（DDI），在制药行业中具有相关性。药物相互作用可能会延迟、减少或增强任何一种药物的吸收，从而降低或增加它们的作用或引起不良反应。信息提取（IE）在识别和提取有关药物相互作用的相关信息方面可能非常有益。我们在此提出一种使用神经词嵌入从文本中提取药物相互作用的方法，以训练机器学习系统。结果表明，我们的系统在提取药物相互作用的任务中与其他系统相比具有竞争力，并且通过从词特征学习和使用深度学习方法可以实现显著改进。我们的研究表明，诸如神经网络和深度学习方法等机器学习技术可以有效地辅助从文本中进行信息提取。我们提出的方法非常适合在未来的研究中发挥重要作用。
