Create app.py

Automatic disease mention extraction is a relevant task due to its various applications in the medical field. During the last decade, many related works have been published, which have accelerated the progress of this research area, but most of them have been carried out in English. In this work, we propose a deep-learning baseline for this task in Spanish. We report an approach based on transfer learning using multilingual BERT and a straightforward post-processing to tackle the problem. Our system does not use any external resources and rely only on efficient fine tuning, which makes it a fair baseline (Micro F1 = 0.5456) for disease mention identification in Spanish using transformer-based models.

app.py

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+import gradio as gr
+
+examples = [["Paciente varón de 35 años con tumoración en polo superior de teste derecho hallada de manera casual durante una autoexploración, motivo por el cual acude a consulta de urología donde se realiza exploración física, apreciando masa de 1cm aproximado de diámetro dependiente de epidídimo, y ecografía testicular, que se informa como lesión nodular sólida en cabeza de epidídimo derecho."], ["Confirmando masa nodular, siendo el tumor adenomatoide de epidídimo la primera posibilidad diagnóstica."], ["Se decide, en los dos casos, resección quirúrgica de tumoración nodular en cola epidídimo derecho, sin realización de orquiectomía posterior."], [""]]
+
+gr.Interface.load("ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT").launch();