View source for 17525432. METHODS AND SYSTEMS FOR AUTOMATICALLY GENERATING CRITERIA FOR CLINICAL TRIALS simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

=METHODS AND SYSTEMS FOR AUTOMATICALLY GENERATING CRITERIA FOR CLINICAL TRIALS=


==Organization Name==

[[:Category:MICROSOFT TECHNOLOGY LICENSING, LLC|MICROSOFT TECHNOLOGY LICENSING, LLC]]


[[Category:MICROSOFT TECHNOLOGY LICENSING, LLC]]

==Inventor(s)==

[[:Category:Nut Limsopatham of Bellevue WA (US)|Nut Limsopatham of Bellevue WA (US)]][[Category:Nut Limsopatham of Bellevue WA (US)]]

[[:Category:Liang Du of Redmond WA (US)|Liang Du of Redmond WA (US)]][[Category:Liang Du of Redmond WA (US)]]

[[:Category:Robin Abraham of Redmond WA (US)|Robin Abraham of Redmond WA (US)]][[Category:Robin Abraham of Redmond WA (US)]]

==METHODS AND SYSTEMS FOR AUTOMATICALLY GENERATING CRITERIA FOR CLINICAL TRIALS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17525432 titled 'METHODS AND SYSTEMS FOR AUTOMATICALLY GENERATING CRITERIA FOR CLINICAL TRIALS

==Simplified Explanation==

The patent application describes methods and systems that use machine learning models to automatically generate criteria for different sections of a clinical study protocol document. 

* Machine learning models are used to identify medical articles related to the clinical study.
* The models analyze these articles to generate recommended criteria for different sections of the protocol document.
* The generated criteria are based on the analysis of the medical articles.

== Potential Applications ==
This technology has potential applications in the field of clinical research and study protocol development.

== Problems Solved ==
The technology solves the problem of manually generating criteria for different sections of a clinical study protocol document, which can be time-consuming and prone to human error.

== Benefits ==
The use of machine learning models to automatically generate criteria for the protocol document can:
* Save time and effort in the protocol development process.
* Improve the accuracy and consistency of the generated criteria.
* Enhance the efficiency of clinical study design and execution.


==Original Abstract Submitted==

The methods and systems may automatically generate criteria for the different sections of the protocol document for a clinical study. The methods and systems use machine learning models to identify medical articles that are associated with the clinical study of a protocol document. The machine learning models analyze the medical articles and generate recommended criteria for the different sections of the protocol document based on the analysis.



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