EHRs records contain valuable information in unstructured format, and standardizing them has the potential to improve healthcare systems and led to save lives. 🏥
The methodology presented, called the Neuro-Symbolic System for Cancer (NSSC), is an end-to-end tool that enhances the named entity recognition and linking tasks. It is presented as an optimization problem to normalize free text as a low-cost solution. Additionally, it is designed as a disease-specific contextual adaptability framework, which allows it to be adapted to other diseases beyond cancer and to others vocabularies. The only ad-hoc step required is the generation of clinical entities, as an annotated corpus is needed to train this model. 📚
I hope you find this work useful and that it contributes to the emerging field of neuro-symbolic systems. By using this hybrid methodology, we can achieve interpretable solutions in a cost-effective manner. ⚖️
NSSC: A Neuro-Symbolic AI System for Enhancing the Accuracy of Named Entity Recognition and Linking from Oncologic Clinical Notes
💻Data Scientist specializing in the analysis and interpretation of medical data.🏥
🕰️ Passionate about exploring the latest advancements in Natural Language Processing (NLP) through hands-on research with cutting-edge open-source Large Language Models (LLMs)⚙️
⛏️ Used to working in community in the Git environment as a software developer and maintainer.
⌨️ Used to program efficiently, scalability and with software design patterns 🔷.
🎯 𝗚𝗶𝘁𝗵𝘂𝗯: https://github.com/Alvaro8gb.
🤗 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲: https://huggingface.co/Alvaro8gb
🖥️ Programing languages I know in preferred order: Python🐍, Java☕️, C 🐧.
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Medical & Biological Engineering & Computing
This journal covers the entire spectrum of biomedical and clinical engineering. The journal aims to present exciting and vital experimental and theoretical developments in biomedical science and technology and to report on advances in computer-based methodologies in these multidisciplinary subjects.
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