A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label
The classification of medical patents play an important role for pharmaceutical company, since medical patens with well labeled can significantly accelerate the process of new drug research. The previous studies using machine learning methods focus on classification the medical patents with single label. However, the classification of medical patents is a multi-label task, and the available data are always incomplete with losing the part information of patents. In this paper, we propose a deep self-learning classification framework that can deal with the incomplete medical patterns with multi-label issue. It consists of a text processor and patent classifier. For the text processor, a professional medical text thesaurus is built via GloVe method, which can learn more specialized vocabulary. For the patent classifier, we adopt a bidirectional long short term memory (Bi-LSTM) model to construct our patent classifier, which can learn the hidden knowledge from medical patents and associate with appropriate labels to medical patents automatically. Furthermore, an advanced focal loss function is design to further improve the classification accuracy. Experiments on the Thomson Reuters dataset demonstrate that our proposed method outperform the other existing methods in terms of precision and recall, when dealing with incomplete medical patents with multi-label issue. ...
KeywordsMulti-label classification Medical patents Bi-LSTM Text process Focal loss
This work was supported by Chongqing research program of technology innovation and application under grant cstc2018jszx-cyztzxX0025 and cstc2017rgzn-zdyfX0020, the National Natural Science Foundation of China under Grant 61602434, the Chongqing research program of key standard technologies innovation of key industries under grant cstc2017zdcy-zdyfX0076, the Youth Innovation Promotion Association CAS, No.2017393 and the Sichuan Science and Technology Department Project 2018GFW0151.
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