Multi-Level Text Classification for Medical Insights Documents

Problem

Our Client was facing challenges  with classification of Medical Insights documents. Earlier classification was done manually, and it takes around 12-man hours a day. They tried developing a model and they were facing below challenges:

  • The datasets is highly imbalance, and they need to predict multi-level prediction of categories
  • Documents in dataset are large and noisy, it is complex to perform a classification task
  • We need to train the model for 4 products and with current approach we will end up with 8 models. And when it comes about deployment strategy, it could be expensive.
  • Deploy a cost saving deployment strategy

Solution

MResult developed a cutting-edge AI solution for multi-level text classification model using technologies such as Python , NLP, NLTK , Transformers, Bert, Deep Learning, Fastai. Machine Learning Algorithms such as, BERT pretrained models , Microsoft Pubmed, SAP Bert.

  • A pipeline of solutions were built to meet multiple requirements such as:
  • Baseline the data sources and metrics for information
  • Collect key points from these data sources into one location
  • Apply NLP and deep learning techniques with Transfer Learning
  • Developed multi-tasking model using deep learning
  • Developed the Deployment strategy using Sagemaker
  • Reduced Deployment cost by 50%

Impact

01

18% improvement in the new modelling strategy. We our approach it got improved from 72% to 92%. Accuracy.

02

We came up with approach for Multi-Tasking Deep learning model. This reduced the deployment cost by 50%

03

Developed Deployment end-to-end deployment strategy for the project using AWS Sagemaker

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