![]() ![]() Text mining and information extraction efforts are gradually being adopted to empower the transformation of unstructured running texts to more structured data representations that can be directly consumed by content analytics and information retrieval infrastructures. ![]() Due to the growing amount of clinical texts, medical and biomedical literature and medicinal chemistry patents, a systematic approach to recognize such entities in order to semantically enrich these documents and enable further relation extraction task is needed. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).Ĭhemical compounds and drugs represent a key biomedical entity of common interest for a range of scientific disciplines, including medicine and clinical research, pharmacology as well as basic biomedical research. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Choose the action you want to assign to the gesture.Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. Choose the gesture that you want to assign to a different action. Select TalkBack settings Customize gestures. Tip: If your Android device has a fingerprint sensor, you can use fingerprint gestures with TalkBack.
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