Optimization of Electronic Health Records: LLM for Clinical Coding and Exploitation

LLM is reshaping the healthcare sector by revolutionizing how machines comprehend and generate human-like text. This breakthrough enhances the processing of medical information, improves documentation efficiency, and contributes to more accurate and contextually relevant healthcare records. One pivotal application is Clinical Coding, which involves converting free-text clinical narratives or medical documentation into standardized codes.

These alphanumeric representations categorize various aspects of patient diagnoses, procedures, medications, and other pertinent healthcare information. Clinical coding plays a crucial role in healthcare management, billing, and research. By assigning specific codes to medical information, healthcare professionals streamline communication, facilitate accurate billing processes, and contribute to the creation of comprehensive health databases. These databases are invaluable for statistical analysis, medical research, and healthcare policy planning.

In this talk, Carlos Rodríguez will explore how the latest generative AI-based NLP models can be utilized for clinical coding and exploitation. Practical examples will be provided during the presentation to illustrate the potential of these models, and the latest trends in this field will be discussed.

About the speaker

Carlos Rodriguez Abellan

GenAI & NLP Practice Lead at Fujitsu

Carlos is a Telecommunication Engineer with a MSc in Signal Theory and Communications, both from the Universidad Politécnica de Madrid. An expert in Artificial Intelligence and Natural Language Processing, he currently leads the practice and development of Generative AI and NLP solutions at FUJITSU, aiming to provide AI-based services to various clients and industries. Previously, he worked on various AI projects at companies such as Telefónica, EY, Vodafone, and British Telecom. Since 2019, he collaborates in various postgraduate programs on subjects related to AI, NLP, or Computer Vision.



Sessions: April 2nd – 3rd 2024
Trainings: April 15th – 19th 2024




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