MedRead is a website which will allow its users to read and analyze medical reports safely and effectively.
Principal Investigator
Name
Denith Pramuditha
Degrees
BEng (Hons) Software Engineering
Institution
University of Westminster
Position Title
Undergraduate
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1297
Initial CDAS Request Approval
Jul 30, 2024
Title
MedRead is a website which will allow its users to read and analyze medical reports safely and effectively.
Summary
The healthcare sector faces an overwhelming influx of daily reports, many of which are unstructured and complex. This complexity hinders the ability to accurately find, isolate, and transform the vast information contained within these documents. To address this challenge, the proposed project aims to develop a comprehensive web application named MedRead. MedRead will leverage Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs) to read, analyze, and summarize textual medical reports. The platform aims to provide precise, comprehensive, and efficient summary reports to medical professionals, thereby enhancing their decision-making processes.
Introduction
MedRead is designed to handle the intricacies of medical language and various report formats. The platform will extract key information, such as diagnoses, treatments, and results, from these reports and summarize them in a clear, actionable format for healthcare professionals. This will save significant time and effort that would otherwise be spent manually reviewing large reports.
NLP and Explainable AI
MedRead's NLP capabilities will address the complexity of medical language and diverse report formats. By analyzing these details, the system will extract essential information and present it in an easily digestible summary. Furthermore, Explainable Artificial Intelligence (XAI) will be integrated into MedRead to ensure accountability and transparency in the automated decisions made by the system. XAI will help users understand how conclusions were reached and what data was prioritized, which is crucial in the medical field where the rationale behind a treatment recommendation is often as important as the treatment itself.
CNN Integration for Medical Imaging
To enhance its capacity for analyzing medical images typically associated with textual reports, MedRead will incorporate CNNs. Pretrained CNNs, which have been extensively tested in image recognition and classification, will be used to extract features from images and correlate them with the text. This integration will provide a more comprehensive view of patient data, further aiding medical specialists.
Research Gap and Opportunity
Current NLP models primarily focus on text data, while medical records often include multimodal data, such as images (CT, MRIs) and structured data (lab results). This project aims to develop models that can seamlessly integrate and interpret multimodal data, providing a more comprehensive understanding of patient health. This advancement will fill a significant gap in existing healthcare technologies.
Objectives
Design, implement, evaluate, and launch a web app for reading and summarizing medical reports without human intervention.
Conduct a literature survey to identify research gaps and relevant technologies.
Integrate existing Electronic Health Records (EHRs) to ensure proper referencing.
Develop a user-friendly UI/UX for easy website navigation.
Use Explainable AI to provide transparency in AI decisions.
System Design: Implement a client-server architecture where the web application communicates with a backend server for processing. Use libraries like NLTK, spaCy, and TensorFlow for NLP techniques such as named entity recognition, text classification, and summarization.
Web and Backend Development: Develop a responsive web application using frameworks like React.js. Implement RESTful APIs for communication between the web app and backend server. Use cloud hosting for data processing and storage.
Introduction
MedRead is designed to handle the intricacies of medical language and various report formats. The platform will extract key information, such as diagnoses, treatments, and results, from these reports and summarize them in a clear, actionable format for healthcare professionals. This will save significant time and effort that would otherwise be spent manually reviewing large reports.
NLP and Explainable AI
MedRead's NLP capabilities will address the complexity of medical language and diverse report formats. By analyzing these details, the system will extract essential information and present it in an easily digestible summary. Furthermore, Explainable Artificial Intelligence (XAI) will be integrated into MedRead to ensure accountability and transparency in the automated decisions made by the system. XAI will help users understand how conclusions were reached and what data was prioritized, which is crucial in the medical field where the rationale behind a treatment recommendation is often as important as the treatment itself.
CNN Integration for Medical Imaging
To enhance its capacity for analyzing medical images typically associated with textual reports, MedRead will incorporate CNNs. Pretrained CNNs, which have been extensively tested in image recognition and classification, will be used to extract features from images and correlate them with the text. This integration will provide a more comprehensive view of patient data, further aiding medical specialists.
Research Gap and Opportunity
Current NLP models primarily focus on text data, while medical records often include multimodal data, such as images (CT, MRIs) and structured data (lab results). This project aims to develop models that can seamlessly integrate and interpret multimodal data, providing a more comprehensive understanding of patient health. This advancement will fill a significant gap in existing healthcare technologies.
Objectives
Design, implement, evaluate, and launch a web app for reading and summarizing medical reports without human intervention.
Conduct a literature survey to identify research gaps and relevant technologies.
Integrate existing Electronic Health Records (EHRs) to ensure proper referencing.
Develop a user-friendly UI/UX for easy website navigation.
Use Explainable AI to provide transparency in AI decisions.
System Design: Implement a client-server architecture where the web application communicates with a backend server for processing. Use libraries like NLTK, spaCy, and TensorFlow for NLP techniques such as named entity recognition, text classification, and summarization.
Web and Backend Development: Develop a responsive web application using frameworks like React.js. Implement RESTful APIs for communication between the web app and backend server. Use cloud hosting for data processing and storage.
Aims
MedRead will provide a web application that effectively reads and summarizes medical reports, enhancing the efficiency and precision of medical professionals. The integration of Explainable AI will increase transparency and trust in the system's decisions, ultimately improving healthcare delivery by reducing time spent on report analysis and enabling faster patient care.
In conclusion, MedRead represents a significant advancement in the use of NLP, CNN, and XAI to transform the processing and utilization of medical reports, promising better healthcare outcomes through enhanced efficiency and accuracy
Collaborators
University of Westminster
Informatics Institute of Technology
Mrs. Sulochana Rupasinghe