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AI solution for analyzing biomedical research articles.
CLIENT
Global B2B SaaS company that offers an AI-powered automation platform and services in 50+ countries.
INDUSTRY
Healthcare
TEAM
Data scientists and software developers with expertise in deep learning, NLP, and cloud computing.
TECHNOLOGIES
MongoDB, PyTorch, Python, Tensorflow, Biopython, Scispacy, Biobert, Google Cloud platform
About the project
Our client, a biomedical research organization, approached us with the challenge of analyzing large volumes of research articles in their field.
They needed a solution that could extract important terms, establish relations between them, and create a summary and knowledge graph of connected terms.
Challenge
The problem was that the amount of research being published was increasing rapidly, and manually analyzing and extracting key information was time-consuming and error-prone. Our solution was to develop an AI-powered system that could automate the analysis process and provide more accurate and efficient results.
Solution
Our team of experts developed a deep learning and natural language processing (NLP) system that could extract important terms from the articles, establish relations between them, and create a summary and knowledge graph of connected terms. The system uses advanced NLP techniques, including Biobert and Scispacy, to accurately analyze the biomedical text.
Impact
The result was a predictive system that could accurately
01
analyze doctor notations
02
determine whether a patient should be included or excluded from a medical program
03
ML techniques implemented in the system can predict a patient's readmission during the 30-day period
04
allow medical professionals to make informed decisions about patient care
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