Clinical Text Mining

Personalized medicine requires the ability to create precision medicine based on the personal health records (EHR, EMR). Problem:  Customer needed a software platform for clinical concept extraction from patient case notes. Methodology: Cenacle built an innovative solution based on the below methodology:
  1. Natural Language Processing (NLP) to identify: diseases, symptoms, medications, procedures etc.
  2. Deep Learning Artificial Intelligence (AI) architecture using RNN with LSTM for sequence labelling
  3. Train the model on annotated text
  4. Classifier built based on UMLS and MIMIC data set models
Results:
  • UMLS: F1 Score: 0.788
  • MIMIC: F1 Score: 0.725
Key concepts used: Text Mining, Natural Language Processing, Named Entity Recognition in Medical Records, Co-reference Resolution, Deep-learning, RNN, Artificial Intelligence. Domain: Healthcare Analytics