GitHub Python ChatGPT Pandas Matplotlib NumPy scikit-learn

The project aims at the extraction of semi-structured textual data (interrelated clinical notes) generated by competent healthcare professionals, within the MIMIC-III database (noteevents). These data will undergo Summarization and Named Entity Recognition + Entity Linking + Relation Extraction processes in order to generate a comprehensive report summarizing the patient’s medical history.

  • 📖 Full Documentation in Italian 🇮🇹 Read
  • 📄 Research Papers in English 🇬🇧 for ITADATA2023/calls Read

⚙️ Project Workflow

Mongo DB

🛠️ Tools used

Mongo DB OpenAI Neo4J MedCat Python MedCat

🗊 Extract final demo report of the ‘Healthcare Summarization’ solution


⚠️ Warning

It is essential to note that MIMIC-III contains sensitive patient data and, therefore, must be handled with the utmost care and in compliance with privacy regulations and institutional policies. Before using the dataset, carefully review and adhere to the guidelines provided by the MIMIC team and consult your local ethics committee.

✅ Project realised for demonstration and educational purposes only

Copyright © 2023 - Healthcare Summarization project carried out for the Big Data Engineering exam held at the University of Naples, Federico II. Realised for demonstration and teaching purposes only.
Antonio Romano, Giuseppe Riccio, Michele Cirillo, Andriy Korsun


<
Previous Post
🦠 SelfTest COVID-19
>
Next Post
🚀 Workflow for Executing CNN Networks on Zynq Ultrascale+ with VITIS AI