Time Optimization for Literature and other text Data using NLP Techniques for a HealthCare Company
Problem
Our Client was facing challenges with Literature reviews as it is a manual, time consuming and tedious process. They were unable to develop a solution as the challenges they faced were:
- Key datasets were available in silos, and it was very time consuming to access it
- Data sources aren’t standardized so it was time consuming to get key insights from data
- Search Engines had to be built to enable faster retrieval
Solution
MResult developed a cutting-edge AI and cognitive solution for users working with molecular data using technologies such as Python , NLP, NLTK , Web Scraping. Machine Learning Algorithms such as XGBoost, Deep Learning, Pytorch, Word2Vec, Roberta, BERT pretrained models.
A pipeline of solutions were built to meet multiple requirements such as:
- Baseline the data sources and metrics for information
- Built a search engine tool
- Collect key points from these data sources into one location
- Apply NLP and ML techniques to summarize data
- Consolidating data sources one by one into larger eco system with a core focus on linking datasets together
- Build tools for automating key processes
- Creating ML pipelines from data preparation to modelling and deployments
