MLM-AI Concepts
Learn more about how MLM-AI works and key concepts that deliver more efficient workflows.
Last updated
Learn more about how MLM-AI works and key concepts that deliver more efficient workflows.
Last updated
addresses all productivity challenges on literature monitoring workflows for safety surveillance. The diagram below outlines key stages in a typical workflow, and their productivity challenges:
One or more literature databases (PubMed, Embase, etc.) are searched, each with their specific query language
Results (title and abstract) are extracted and collated together
Articles are de-duplicated, removing repeated and articles previously seen on past runs
Abstracts are then screened for relevant safety events
If screen is positive, the article may then proceed for a full text review
Results are quality controlled (QC) and must be exported in a suitable format for further processing downstream
The Biologit platform addresses the above challenges with an integrated system that is easy to adopt and to scale to your needs. In Biologit the following components were built from the ground up with the needs of literature monitoring of safety events, working seamlessly together:
Biologit Database: comprehensive database of scientific and medical literature covering global and regional sources
Robust and validated AI models tailored for pharmacovigilance and safety surveillance
Easy to use web application delivering integrated workflow, collaboration, reporting and integration capabilities
High quality results for literature monitoring of adverse events and possible new risks for human and veterinary products, medical devices, nutraceuticals and cosmetics.
The literature screening process can be setup in the platform with these simple steps:
Once configured, Monitors produce results periodically, or on request.
Tag
Description
Suspected AE
The abstract or full text may contain the description of an adverse event. Thus, MLM-AI flagged the abstract as a candidate for further screening.
Suspected Case
The abstract or full text may contain the description of a case (identifiable patient).
Animal/In-Vitro
The abstract describes animal or in-vitro studies.
Special Situations
Any detected special situation (elderly, pediatric, pregnancy) is also flagged as a tag.
Identifiable Patient
A confirmed patient mention (ex: "a 72-year old woman" etc)
Why "suspected" adverse events?
A "suspected" event indicates an event may have been described in the full text and indirectly mentioned in the abstract and was considered by MLM-AI as worth further inspection.
Suspected events are drug-agnostic: they can refer to any drug or treatment mentioned in the abstract.
Learn more:
Follow the links below to:
The is a comprehensive and continuously updated repository of scientific literature, ready for compliant regulatory searches, integrating databases of global and regional reach into an easy to use interface. It provides:
Full integration into for fast and audit-ready literature screening.
Users defining the search criteria for a product, literature sources to search, and workflow details. This is a one-time setup.
Search results from an external source.
Results are de-duplicated and presented to users as . They are also tagged by AI models for faster screening. (see AI Tags below)
Users can then according to their workflow. during article assessment:
Full text articles can be uploaded to the workflow as , they are permanently stored in the platform for future reference.
can be directly requested from the system.
On completion assessment results can be in various formats for further processing (addition to the safety database) or reporting.
Every article presented to users receives a number of tags based on AI predictions. These tags can be used to filter and rank results,
See our page on for approaches that integrate AI tags into your screening workflow.
See the for more details about how MLM-AI makes predictions.
