Prior Surgery Detection in Radiology Reports

identify prior surgeries from radiology reports using natural language processing 

The project aims to identify prior surgeries from radiology reports using natural language processing (NLP) techniques. This application is significant in the medical field, assisting healthcare professionals in quickly identifying patients' surgical histories, thereby improving the efficiency and accuracy of medical diagnostics and treatments.

Role and Team Collaboration:

In this project, I was responsible for developing the deep learning model, implementing text data processing techniques, and visualizing the results. I collaborated with a team of data scientists and medical professionals to ensure the accuracy and relevance of the application.

Outcome:

The project involves reading and preprocessing radiology report text for analysis. A deep learning model is used to classify reports based on the presence of prior surgeries. Techniques are employed to balance the dataset and improve model performance. The project also includes word cloud visualization to understand the common terminologies associated with prior surgeries.

Challenges and Solutions:

One of the main challenges was accurately processing and classifying unstructured text data. To address this, we implemented advanced NLP techniques and a robust deep learning model to ensure accurate classification.

Impact and Applications:

The application can be used in hospitals and clinics for quick scanning of patient histories, improving the efficiency of medical record analysis. It can also aid in medical research by providing insights into surgical trends and outcomes.

Conclusion and Learnings:

This project demonstrates the potential of NLP and machine learning in enhancing medical record analysis. It highlights the importance of data preprocessing and model tuning in handling real-world text data.

Artifacts Created:

• Repository Link: Prior Surgery Detection on GitHub