With the strides taken in technology, we are at a cusp of a revolution in Artificial Intelligence, which will have an impact on our everyday life. We are already seeing its increased usage in fraud detection, recommendation engine, crop classification etc. AI methods and systems such as Natural Language Processing (NLP), Machine Learning, and Neural Networks are now at our disposal to not only synergize but also improve with the current workflow.
Following are the broad two categories of usage AI technologies in ICSR Processing:
Ingestion of structured and unstructured content- Comprises of components for reading incoming case intake information via XML, Docx, images including PDF and PDF text including forms-tables. Here OCR/ICR along with NLP/machine learning is used to extract ICSR information from information sources in a regulatory compliant manner.
AI for decision making:- Sometimes, the quality of information available in ICSR is poor. In such scenarios, Semi-supervised or unsupervised learnings play a major role in devising hypothesis. For example, building Unlisted Events and Drugs Correlation, Causality Classifiers etc., specific type of Neural Networks are built and improvised with training over a period. These are faster and more accurate compared to other methods.
Key considerations of Artificial Intelligence in ICSR processing –
As with any Artificial Intelligence system implementations as described above, doesn’t intend to completely replace human element, but complements the process, and helps in identifying and bringing out seemingly hidden relationships for ensuring accurate ICSR processing in Pharmacovigilance.
