Extract Data Elements
Content analysis for regulatory data mining and beyond
Designed specifically for Life Sciences, cune-Distiller is built on Cunesoft's Content Analysis Framework and is optimized to enable comprehensive data extraction from large volumes of documents.
For IDMP and xEVMPD readiness, we have a configuration designed to automatically extract data from submission documents, SmPCs and xEVPRM messages.
Extract Data Elements
Multiple Document Types
Data mining, mapping consolidation, curation, maintenance and export for SMPC’s, eCTD Module 3, xEVPRM, SPL and more
Compare SMPC versions – auto update xEVPRM messages
Data mine current SMPC’s and automatically receive notifications for changes and business rules from newer SMPC version. Available for 26 languages.
Automated Document Redaction
EMA Policy 70 requires the redaction of confidential information within clinical study reports, protocols etc.
Data Base Quality Assurance
Identifying data inconsistencies across multiple data stores and clean data automatically.
Automated eTMF Metadata Tagging
A seamless eTMF process that extracts data from clinical trial documents (i.e. physician CV’s etc.)
Safety Data Analysis
Automated analyses safety reports, adverse event reports and other safety related documentation to automate safety updates, adverse event reporting etc.
In the past, many were skeptical about automated text mining. And today, the common belief that text mining can do the easy 20% but does not help with the difficult 80% of data extraction is already outdated. There are significant advances in terms of technology and modern data mining algorithms.
Modern technologies like cune-Distiller use several different extraction strategies in parallel: neural networks, natural language processing, fuzzy logic, and deep learning. When combined the right way, information can be extracted from virtually any electronic document. The result is quite impressive. Moreover, it will become even better over time. Artificial intelligence software learns what result is expected and improves its settings. Of course, these techniques can be used for many additional use cases.
cune-Distiller uses many techniques including artificial intelligence (AI) to reach its full potential. Depending on the specific needs for each task, one or multiple techniques get applied.
AI technology is very complex and difficult to develop. Cunesoft invested many resources to develop an algorism which serves the needs of the life science industry. There are many possible use cases for this multipurpose tool. Below you see the six steps during the data mining process applicable in IDMP iteration 1.
*click to see the extracted Information
cune-Distiller automatically extracts various data types from PDF documents including unstructured information such as clinical particulars.
In this 26-page report you will learn about the overall project approach as well as the results. To receive the full POC summary report please apply here.
The system automatically maps extracted data entities with regulatory data bases GiNAS or EV Codes. Furthermore identified indications are being mapped with MedDRA codes automatically. At any point in time users can manually override the systems suggestions.
The platform can be configured to extract information from additional document types other than those found in module 3 of the eCTD. Find out how Cunesoft can help you find more possibilities in your content.