Atkinson John, Rivas Alejandro
Department of Computer Sciences, Universidad de Concepcion, Concepcion 3349001, Chile.
IEEE Trans Inf Technol Biomed. 2008 Nov;12(6):714-22. doi: 10.1109/TITB.2008.920793.
Most of the biomedicine text mining approaches do not deal with specific cause--effect patterns that may explain the discoveries. In order to fill this gap, this paper proposes an effective new model for text mining from biomedicine literature that helps to discover cause--effect hypotheses related to diseases, drugs, etc. The supervised approach combines Bayesian inference methods with natural-language processing techniques in order to generate simple and interesting patterns. The results of applying the model to biomedicine text databases and its comparison with other state-of-the-art methods are also discussed.
大多数生物医学文本挖掘方法并未处理那些可能解释所发现内容的特定因果模式。为了填补这一空白，本文提出了一种从生物医学文献中进行文本挖掘的有效新模型，该模型有助于发现与疾病、药物等相关的因果假设。这种监督式方法将贝叶斯推理方法与自然语言处理技术相结合，以生成简单且有趣的模式。本文还讨论了将该模型应用于生物医学文本数据库的结果及其与其他最先进方法的比较。
