IP Library Patent Application 17180678
Patent Application
App. No. 17/180,678

TRAINING A MACHINE LEARNING MODULE AND UPDATING A RULES ENGINE TO DETERMINE MEDICAL BEST PRACTICE RECOMMENDATIONS FOR USER ENTERED MEDICAL OBSERVATIONS

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Quick Facts
Patent No.
US None
App. No.
17/180,678
Abstract

Provided are a computer program product, system, and method for determining real-time changes to content entered into a user interface to generate results for the content. In response to determining that entry of first content in a user input field rendered in a user interface is completed, the first content is provided to a classification program to classify into a first machine classification to provide to a rules engine to determine a first machine determined proposition. The first machine determined proposition is rendered in the user interface. A determination is made of second content in the user input field from the user that differs from the first content. The second content is provided to the classification program to classify into a second machine classification to provide to the rules engine to determine a second machine determined proposition rendered in the user interface with the second content.

Claims (56)

1 - 22 . (canceled)

23 . A computer program product for determining medical best practice recommendations for user entered medical observations, the computer program product comprising a computer readable storage medium having computer readable program code embodied therein that is executable to perform operations, the operations comprising:

receiving a medical observation of a patient from a user;

processing, by a classification program, implementing a machine learning module, the received medical observation to classify into a medical finding based on the received medical observation;

processing, by a rules engine, comprising a data structure that provides best practice recommendations for each of possible medical findings outputted from the classification program, the medical finding to determine a medical best practice recommendation for the medical finding;

transmitting the medical finding and the medical best practice recommendation to render in a user interface;

receiving indication of the user rejecting the medical finding rendered in the user interface; and

training the machine learning module comprising the classification program to output a preferred medical finding, provided to replace the medical finding transmitted to the user interface, based on the medical observations, in response to receiving the user rejecting the medical finding.

24 . The computer program product of claim 23 , wherein the operations further comprise:

receiving indication of the user rejecting the medical best practice recommendation; and

updating the rules engine to associate a preferred medical best practice recommendation with the medical finding rendered in the user interface in response to the receiving of the indication of the user rejecting the medical best practice recommendation for the medical finding without rejecting the medical finding, wherein the updated rules engine outputs the preferred medical best practice recommendation in response to the machine learning module outputting the medical finding.

25 . The computer program product of claim 24 , wherein the operations further comprise:

updating the rules engine to not associate the medical best practice recommendation rendered in the user interface with the medical finding.

26 . The computer program product of claim 23 , wherein the operations further comprise:

updating the rules engine to provide a preferred medical best practice recommendation for the preferred medical finding and not to output the medical best practice recommendation for the medical finding in response to receiving the indication of the user rejecting the medical finding and rejecting the medical best practice recommendation.

27 . The computer program product of claim 23 , wherein the operations further comprise:

updating the rules engine to provide a preferred medical best practice recommendation for the preferred medical finding in response to the receiving indication of the user rejecting the medical best practice recommendation and rejecting the medical finding rendered in the user interface.

28 . The computer program product of claim 27 , wherein the preferred medical best practice recommendation and the preferred medical finding are received from the user when rejecting the medical best practice recommendation and the medical finding rendered in the user interface.

29 . The computer program product of claim 23 , wherein the rules engine uses a decision tree of rules to determine the medical best practice recommendation for the medical finding, wherein the decision tree includes rules to map medical findings from the classification program to medical best practice recommendations to output one of the medical best practice recommendations based on one of the possible medical findings outputted by the classification program.

30 . A system for determining medical best practice recommendations for user entered medical observations, comprising:

a processor; and

a computer readable storage medium having computer readable program code embodied that when executed by the processor performs operations, the operations comprising: receiving a medical observation of a patient entered into a user interface;

receiving a medical observation of a patient from a user;

processing, by a classification program, implementing a machine learning module, the received medical observation to classify into a medical finding based on the received medical observation;

processing, by a rules engine, comprising a data structure that provides best practice recommendations for each of possible medical findings outputted from the classification program, the medical finding to determine a medical best practice recommendation for the medical finding;

transmitting the medical finding and the medical best practice recommendation to render in the user interface;

receiving indication of the user rejecting the medical finding rendered in the user interface; and

training the machine learning module comprising the classification program to output a preferred medical finding, provided to replace the medical finding transmitted to the user interface, based on the medical observations, in response to receiving the user rejecting the medical finding.

31 . The system of claim 30 , wherein the operations further comprise:

receiving indication of the user rejecting the medical best practice recommendation; and

updating the rules engine to associate a preferred medical best practice recommendation with the medical finding rendered in the user interface in response to the receiving the indication of the user rejecting the medical best practice recommendation for the medical finding without rejecting the medical finding, wherein the updated rules engine outputs the preferred medical best practice recommendation in response to the machine learning module outputting the medical finding.

32 . The system of claim 31 , wherein the operations further comprise:

updating the rules engine to not associate the medical best practice recommendation rendered in the user interface with the medical finding.

33 . The system of claim 30 , wherein the operations further comprise:

updating the rules engine to provide a preferred medical best practice recommendation for the preferred medical finding and not to output the medical best practice recommendation for the medical finding in response to receiving the indication of the user rejecting the medical finding and rejecting the medical best practice recommendation.

34 . The system of claim 30 , wherein the operations further comprise:

updating the rules engine to provide a preferred medical best practice recommendation for the preferred medical finding in response to the receiving indication of the user rejecting the medical best practice recommendation and rejecting the medical finding rendered in the user interface.

35 . The system of claim 34 , wherein the preferred medical best practice recommendation and the preferred medical finding are received from the user when rejecting the medical best practice recommendation and the medical finding rendered in the user interface.

36 . The system of claim 30 , wherein the rules engine uses a decision tree of rules to determine the medical best practice recommendation for the medical finding, wherein the decision tree includes rules to map medical findings from the classification program to medical best practice recommendations to output one of the medical best practice recommendations based on one of the possible medical findings outputted by the classification program.

37 . A computer implemented method for determining medical best practice recommendations for user entered medical observations, comprising:

receiving a medical observation of a patient from a user;

processing, by a classification program, implementing a machine learning module, the received medical observation to classify into a medical finding based on the received medical observation;

processing, by a rules engine, comprising a data structure that provides best practice recommendations for each of possible medical findings outputted from the classification program, the medical finding to determine a medical best practice recommendation for the medical finding;

transmitting the medical finding and the medical best practice recommendation to render in a user interface;

receiving indication of the user rejecting the medical finding rendered in the user interface; and

training the machine learning module comprising the classification program to output a preferred medical finding, provided to replace the medical finding transmitted to the user interface, based on the medical observations, in response to receiving the user rejecting the medical finding.

38 . The method of claim 37 , further comprising:

receiving indication of the user rejecting the medical best practice recommendation; and

updating the rules engine to associate a preferred medical best practice recommendation with the medical finding rendered in the user interface in response to the receiving the indication of the user rejecting the medical best practice recommendation for the medical finding without rejecting the medical finding, wherein the updated rules engine outputs the preferred medical best practice recommendation in response to the machine learning module outputting the medical finding.

39 . The method of claim 38 , further comprising:

updating the rules engine to not associate the medical best practice recommendation rendered in the user interface with the medical finding.

40 . The method of claim 37 , further comprising:

updating the rules engine to provide a preferred medical best practice recommendation for the preferred medical finding and not to output the medical best practice recommendation for the medical finding in response to receiving the indication of the user rejecting the medical finding and rejecting the medical best practice recommendation.

41 . The method of claim 37 , further comprising:

updating the rules engine to provide a preferred medical best practice recommendation for the preferred medical finding in response to the receiving indication of the user rejecting the medical best practice recommendation and rejecting the medical finding rendered in the user interface.

42 . The method of claim 38 , wherein the preferred medical best practice recommendation and the preferred medical finding are received from the user when rejecting the medical best practice recommendation and the medical finding rendered in the user interface.

Assignments (8)
SECURITY AGREEMENT (NOTES) Recorded Jul 1, 2025
From: VIRTUAL RADIOLOGIC CORPORATION; RADIOLOGY PARTNERS, INC.
To: WILMINGTON TRUST, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 071781/0802 →
RELEASE OF SECURITY INTEREST Recorded Jul 1, 2025
From: WILMINGTON TRUST, NATIONAL ASSOCIATION
To: RADIOLOGY PARTNERS, INC.; VIRTUAL RADIOLOGIC CORPORATION
Reel/Frame 071575/0916 →
RELEASE (REEL 066660 / FRAME 0573) Recorded Jul 1, 2025
From: BARCLAYS BANK PLC
To: VIRTUAL RADIOLOGIC CORPORATION; RADIOLOGY PARTNERS, INC.
Reel/Frame 071781/0746 →
SECURITY AGREEMENT (FIRST LIEN) Recorded Jul 1, 2025
From: VIRTUAL RADIOLOGIC CORPORATION; RADIOLOGY PARTNERS, INC.
To: BARCLAYS BANK PLC, AS AGENT
Reel/Frame 071781/0790 →
SECURITY INTEREST Recorded Feb 26, 2024
From: RADIOLOGY PARTNERS, INC.; VIRTUAL RADIOLOGIC CORPORATION
To: WILMINGTON TRUST, NATIONAL ASSOCIATION
Reel/Frame 066564/0185 →
SECURITY INTEREST Recorded Feb 26, 2024
From: RADIOLOGY PARTNERS, INC.; VIRTUAL RADIOLOGIC CORPORATION
To: WILMINGTON TRUST, NATIONAL ASSOCIATION
Reel/Frame 066564/0242 →
SECURITY AGREEMENT (FIRST LIEN) Recorded Feb 22, 2024
From: RADIOLOGY PARTNERS, INC.; VIRTUAL RADIOLOGIC CORPORATION
To: BARCLAYS BANK PLC, AS AGENT
Reel/Frame 066660/0573 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 1, 2021
From: TOBIAS, THOMAS N.; KOTTLER, NINA; MITSKY, JASON R.; LIANG, JOYCE; DENNEY, KELLY; CROXALL, KEVIN; BERKEY, TELFORD; SALZWEDEL, JAI
To: RADIOLOGY PARTNERS, INC.
Reel/Frame 055449/0682 →