View source for 17958245. PATIENT DATA REMOVAL simplified abstract (Cilag GmbH International)

=PATIENT DATA REMOVAL=


==Organization Name==

[[:Category:Cilag GmbH International|Cilag GmbH International]]


[[Category:Cilag GmbH International]]

==Inventor(s)==

[[:Category:Frederick E. Shelton, Iv of Hillsboro OH (US)|Frederick E. Shelton, Iv of Hillsboro OH (US)]][[Category:Frederick E. Shelton, Iv of Hillsboro OH (US)]]

[[:Category:Matthew David Cowperthwait of Cincinnati OH (US)|Matthew David Cowperthwait of Cincinnati OH (US)]][[Category:Matthew David Cowperthwait of Cincinnati OH (US)]]

[[:Category:Christopher Waid of Cincinnati OH (US)|Christopher Waid of Cincinnati OH (US)]][[Category:Christopher Waid of Cincinnati OH (US)]]

[[:Category:Taylor W. Aronhalt of Loveland OH (US)|Taylor W. Aronhalt of Loveland OH (US)]][[Category:Taylor W. Aronhalt of Loveland OH (US)]]

[[:Category:Jason L. Harris of Lebanon OH (US)|Jason L. Harris of Lebanon OH (US)]][[Category:Jason L. Harris of Lebanon OH (US)]]

==PATIENT DATA REMOVAL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17958245 titled 'PATIENT DATA REMOVAL

==Simplified Explanation==

The abstract of the patent application describes a data system that can adjust input data to a machine learning model based on changes in patient consent. The system detects changes in consent, identifies private data affected by the change, determines which machine learning model the private data contributed to, and decides whether to replace the private data in the input data with replacement data.

* The data system detects changes in patient consent.
* It identifies private data impacted by the change.
* It determines which machine learning model the private data contributed to.
* It decides whether to replace the private data in the input data with replacement data.

== Potential Applications ==
This technology could be applied in healthcare settings where patient consent is crucial for data processing and analysis. It could also be used in other industries where sensitive data is involved, such as finance or legal services.

== Problems Solved ==
This technology addresses the issue of maintaining data privacy and compliance with changing consent regulations. It ensures that sensitive information is handled appropriately in machine learning models.

== Benefits ==
The system provides a way to adapt to changes in patient consent without compromising the integrity of the machine learning models. It helps organizations stay compliant with data privacy laws and regulations.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in healthcare analytics companies that use machine learning models to analyze patient data while ensuring compliance with privacy regulations.

== Possible Prior Art ==
One possible prior art for this technology could be systems that automatically redact sensitive information in documents based on user permissions or access levels.

=== What are the potential ethical implications of using this technology in healthcare settings? ===

Using this technology in healthcare settings raises concerns about patient privacy and consent. Organizations must ensure that they are transparent with patients about how their data is being used and obtain proper consent before making any adjustments to the data.

=== How can organizations ensure the security of the replacement data used in the input data for machine learning models? ===

Organizations can implement robust encryption and access control measures to secure the replacement data used in the input data. Regular audits and monitoring can also help detect any unauthorized access or breaches.


==Original Abstract Submitted==

A data system may adjust the input data to a machine learning model based on a change in a consent associated with a patient. The data system may detect the change in the consent associated with the patient. The data system may identify private data associated with the change in the consent. The data system may identify a machine learning model to which the private data has contributed. The data system may determine input data that has contributed to the machine learning model. The input data may include the private data. The data system may determine, based on the change in the consent associated with the private data, whether to replace the private data in the input data with replacement data. The data system may adjust the input data based on the determination of whether to replace the private data in the input data with replacement data.



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