Helped a leading pharmaceutical company to digitize its “PlaceHolder Document...Read More
The client is an Indian multinational pharmaceutical company. The client had recently acquired another leading pharmaceutical company and was going through a major phase of digitization of its existing documentation and related processes.
The client had thousands of medicine preparation templates. The client was updating the values in the blank spaces manually that was taking a long time, and prone to human error.
As part of the Quality Systems initiative (a program where all existing documents are digitized and end-users are given an interface), the client wished to automate the following process:
We developed a traditional Named Entity Recognition model using POS tags and another using LSTM. Both models were able to detect the meaningful chemical entities on either side of the blank spaces in the document and replaced the blanks with these texts.
With the entire manual process successfully automated, the manual effort was reduced to a staggering 5% of the initial, and human resources costs were reduced greatly as well.
The solution was deployed on Rapidminer to provide a seamless interface to run the process for 100s of documents. As a result, the time for the entire process was reduced from hours to minutes.
Aptus Data Labs used the following methodology for automating the process to resolve the existing challenge. Aptus Data Labs:
Python, Gensim, Keras, Powershell Scripts and Rapidminer.