MLM-AI Concepts
Learn more about how MLM-AI works and key concepts that deliver more efficient workflows.
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.
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
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 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.
Learn more:
- See our page on AI-Enabled Screening Workflows for approaches that integrate AI tags into your screening workflow.
Follow the links below in order to:
Last modified 8mo ago