The data generated by the structured electronic medical records is helpful for mining and extracting medical data, and it is an effective way to make effective use of valuable data resources. However, the hospitals have accumulated a large number of unstructured data in electronic medical records, which cannot be effectively searched, resulting in serious waste of resources. In this paper, we study the problem of extracting attribute values from the unstructured text in electronic medical records. By observing intestinal cancer diagnostic texts, our attributes have two categories-discriminative attributes and extractive attributes, which use the text classification and the sequence labeling to tackle attribute values extraction problems. For discriminative attributes, we firstly divide the text into sentences/segments as instances. Secondly, we finetune the pre-trained word embedding to capture domain-specific semantics/knowledge. Thirdly, we also use an attention mechanism to select the most important instance for different attribute extractors. Finally, multi-tasking learning is used to share useful information to get better experimental results. For extractive attributes, we propose a novel model to get attribute values, including the BiLSTM layer, the CNN layer and the CRF layer. In particular, we use BiLSTM and CNN to learn text features and CRF as the last layer of the model. Experiments have shown that our method is superior to several competitive baseline methods.
Relied on artificial intelligence major, the importance of innovation and entrepreneurship education in applied undergraduate colleges were analyzed. The problems and reform measures of the innovation and entrepreneurship talent training model for artificial intelligence majors in applied undergraduate colleges were proposed and expounded. Innovation and entrepreneurship education was promoted through four aspects: target orientation, curriculum system construction, industry-teaching integration and competition promotion, which could comprehensively improve students' innovation and entrepreneurship ability and comprehensive quality. Origin was mainly used to analyze the artificial intelligence curriculum system. Compared with the traditional talent training mode, the proportion of practical courses in the training program had been significantly improved, and more attention was paid to the cultivation of students' innovation and entrepreneurship ability. Other engineering majors could also get reference from this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.