Learn more about how MLM-AI works and key concepts that deliver more efficient workflows.
Literature Monitoring with MLM-AI
To introduce how MLM-AI makes Medical Literature Monitoring more efficient, below is an outline of the traditional workflow for this process:
A typical literature monitoring workflow comprises these stages:
One or more databases (PubMed, Embase, Google Scholar, 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 QA'ed and exported for further processing downstream.
All the above steps were taking into account when building MLM-AI's integrated workflow:
MLM-AI automates searching and de-duplication of results from multiple sources, presenting a single, clean view of all retrieved abstracts.
MLM-AI searches from a growing number of sources automatically, including PubMed, DOAJ, and Crossref. See the FAQ on data sources and the latest release notes for more information.
Users can also upload their own search results from sources that support exporting into a reference management format, and perform the remaining workflow from inside MLM-AI.
Using AI Tags (see below), MLM-AI also enables workflows that rank and filter irrelevant articles based on predictions from our AI models. This further removes time from processing large volumes of inbound articles.
Finally, the remaining steps of the workflow are all performed from within a single tool, with all actions recorded into an audit log and with full visibility by the entire team.
Monitors and Reviews
In MLM-AI, the screening process has the following steps:
Users configure a monitor defining the search criteria for a product, literature sources to search, and workflow details.
Once configured, results are produced periodically, de-duplicated and presented to users as Reviews. They are also tagged by our AI models for faster screening (see AI Tags below).
Depending on the desired customer workflow, results may also be filtered according to their AI tag.
Users can then screen abstracts in reviews according to their workflow, and export results upon screening completion.
Screening results in MLM-AI
Every abstract presented to users receives a number of tags based on predictions made by MLM-AI. These tags can be used to filter and rank results, facilitating more efficient workflows.
Example tags associated to an abstract
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.
The abstract or full text may contain the description of a case (identifiable patient).
The abstract describes animal or in-vitro studies.
Any detected special situation (elderly, pediatric, pregnancy) is also flagged as a tag.
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 alluded to 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.