IP Library › Granted Patent US 12,462,911
Granted Patent B2
US 12,462,911 · App. 17/207,582 · Granted Nov 4, 2025

Clinical concept identification, extraction, and prediction system and related methods

Inventors: Michael Lucas (Chicago, IL); Jonathan Ozeran (Chicago, IL); Jason Taylor (Chicago, IL); Louis Fernandes (Chicago, IL)
Assignee: TEMPUS AI, INC.
G16H15/00G06F40/295G06F40/30G16H10/20G16H10/60G16H70/00
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Quick Facts
Patent No.
US 12,462,911
App. No.
17/207,582
Granted
Nov 4, 2025
Kind
B2
Abstract

A method for determining whether a patient may be enrolled into a clinical trial includes the steps of examining the patient's medical record from an electronic health record system, deriving a plurality of first concepts from the medical record, normalizing each concept in the plurality of first concepts to produce, for each normalization, a normalized concept, comparing each normalized concept to a list of study criteria, to indicate if the normalized concept meets the criteria, and if each criteria is met, indicating that the patient is not ineligible for enrollment in the clinical trial

Claims (56)

1 . A computer-implemented method for determining whether a subject may be enrolled into a clinical trial, the computer-implemented method comprising:

storing, via one or more processors, a plurality of abstraction categories in a database wherein each abstraction category includes a plurality of fields and each field corresponds to a concept type;

accessing, via one or more processors, a medical record of a subject from an electronic health record system wherein the medical record includes a plurality of text words;

deriving, via one or more processors, a plurality of first concepts from the medical record by:

(i) identifying, via one or more processors, a match to a first concept in the plurality of first concepts, the plurality of first concepts not designated as a predetermined authority and including two or more tiers of concepts;

(ii) referencing, via one or more processors, the first concept with an entity in a database of related concepts;

(iii) identifying, via one or more processors, a match from the entity to a second concept in a second list of concepts designated in the predetermined authority, wherein the second concept is included in one of the two or more tiers of concepts, and wherein the predetermined authority is not directly linked to the plurality of first concepts except by a relationship to the entity;

(iv) retrieving, via one or more processors, a degree of specificity comprising selection criteria identifying one or more tiers of the two or more tiers of concepts, other than the one tier including the second concept; and

(v) normalizing, via one or more processors, the second concept to a third concept, of a plurality of third concepts, included in the one or more tiers of the two or more tiers of concepts, based at least in part on the second concept not satisfying the selection criteria;

comparing, via one or more processors, a list of study criteria associated with the clinical trial to the plurality of third concepts, to indicate if the subject meets the list of study criteria; and

generating, via one or more processors, an indication of eligibility of the subject for enrollment in the clinical trial based on the comparing.

2 . The computer-implemented method of claim 1 , wherein deriving the plurality of first concepts from the medical record comprises using a constituency tree.

3 . The computer-implemented method of claim 1 , wherein deriving the plurality of first concepts from the medical record comprises using a name entity recognition model.

4 . The computer-implemented method of claim 3 , wherein the name entity recognition model comprises a conditional random field.

5 . The computer-implemented method of claim 3 , wherein the name entity recognition model comprises a convolutional neural network.

6 . A computer-implemented method for determining whether a subject may be enrolled into a clinical trial, comprising:

examining, via one or more processors, a medical record of the subject from an electronic health record system;

deriving, via one or more processors, a plurality of first concepts from the medical record by:

(i) identifying, via one or more processors, a match to a first concept in the plurality of first concepts, the plurality of first concepts not designated as a predetermined authority and including two or more tiers of concepts;

(ii) referencing, via one or more processors, the first concept with an entity in a database of related concepts;

(iii) identifying, via one or more processors, a match from the entity to a second concept in a second list of concepts designated in the predetermined authority, wherein the second concept is included in one of the two or more tiers of concepts, and wherein the predetermined authority is not directly linked to the plurality of first concepts except by a relationship to the entity;

(iv) retrieving, via one or more processors, a degree of specificity comprising selection criteria identifying one or more tiers of the two or more tiers of concepts, other than the one tier including the second concept; and

(v) normalizing, via one or more processors, the second concept to a third concept, of a plurality of third concepts, included in the one or more tiers of the two or more tiers of concepts, based at least in part on the second concept not satisfying the selection criteria;

comparing, via one or more processors, a list of study criteria associated with the clinical trial to the plurality of third concepts, to indicate if the subject meets the list of study criteria; and

generating, via one or more processors, an indication of eligibility of the subject for enrollment in the clinical trial based on the comparing.

7 . The computer-implemented method of claim 6 , wherein deriving the plurality of first concepts from the medical record comprises using a constituency tree.

8 . The computer-implemented method of claim 6 , wherein deriving the plurality of first concepts from the medical record comprises using a name entity recognition model.

9 . The computer-implemented method of claim 8 , wherein the name entity recognition model comprises a conditional random field.

10 . The computer-implemented method of claim 8 , wherein the name entity recognition model comprises a convolutional neural network.

11 . A computing system for determining whether a subject may be enrolled into a clinical trial, the computing system comprising:

a computer including a processing device, the processing device configured to:

examine a medical record of the subject from an electronic health record system;

derive a plurality of first concepts from the medical record by:

(i) identifying a match to a first concept in the plurality of first concepts, the plurality of first concepts not designated as a predetermined authority and including two or more tiers of concepts;

(ii) referencing the first concept with an entity in a database of related concepts;

(iii) identifying a match from the entity to a second concept in a second list of concepts designated in the predetermined authority, wherein the second concept is included in one of the two or more tiers of concepts, and wherein the predetermined authority is not directly linked to the plurality of first concepts except by a relationship to the entity;

(iv) retrieving a degree of specificity comprising selection criteria identifying one or more tiers of the two or more tiers of concepts, other than the one tier including the second concept; and

v) normalizing the second concept to a third concept, of a plurality of third concepts, included in the one or more tiers of the two or more tiers of concepts, based at least in part on the second concept not satisfying the selection criteria;

compare a list of study criteria associated with the clinical trial to the plurality of third concepts, to indicate if the subject meets the list of study criteria; and

generate an indication of eligibility of the subject for enrollment in the clinical trial based on the comparing.

12 . The computing system of claim 11 , wherein the processing device is further configured to:

use a constituency tree.

13 . The computing system of claim 11 , wherein the processing device is further configured to:

use a name entity recognition model.

14 . The computing system of claim 13 , wherein the name entity recognition model comprises a conditional random field.

15 . The computing system of claim 13 , wherein the name entity recognition model comprises a convolutional neural network.

16 . A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to:

examine a medical record of a subject from an electronic health record system;

derive a plurality of first concepts from the medical record by:

(i) identifying a match to a first concept in a plurality of first concepts, the plurality of first concepts not designated as a predetermined authority and including two or more tiers of concepts;

(ii) referencing the first concept with an entity in a database of related concepts;

(iii) identifying a match from the entity to a second concept in a second list of concepts designated in the predetermined authority, wherein the second concept is included in one of the two or more tiers of concepts, and wherein the predetermined authority is not directly linked to the plurality of first concepts except by a relationship to the entity;

(iv) retrieving, via one or more processors, a degree of specificity comprising selection criteria identifying one or more tiers of the two or more tiers of concepts, other than the one tier including the second concept; and

(v) normalizing the second concept to a third concept, of a plurality of third concepts, included in the one or more tiers of the two or more tiers of concepts, based at least in part on the second concept not satisfying the selection criteria; and

compare a list of study criteria associated with a clinical trial to the plurality of third concepts, to indicate if the subject meets the list of study criteria; and

generate an indication of eligibility of the subject for enrollment in the clinical trial based on the comparing.

Assignments (4)
RELEASE OF SECURITY INTEREST Recorded May 13, 2026
From: ARES CAPITAL CORPORATION, AS COLLATERAL AGENT
To: TEMPUS AI, INC. (F/K/A TEMPUS LABS, INC.)
Reel/Frame 075608/0784 →
CHANGE OF NAME Recorded Feb 9, 2024
From: TEMPUS LABS, INC.
To: TEMPUS AI, INC.
Reel/Frame 066544/0110 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 27, 2022
From: LUCAS, MICHAEL; TAYLOR, JASON; OZERAN, JONATHAN; FERNANDES, LOUIS
To: TEMPUS LABS, INC.
Reel/Frame 061557/0437 →
SECURITY INTEREST Recorded Sep 22, 2022
From: TEMPUS LABS, INC.
To: ARES CAPITAL CORPORATION, AS COLLATERAL AGENT
Reel/Frame 061506/0316 →
Continuity (3)
Continuation 16702510 · Dec 3, 2019
Provisional Application 62774854 · Dec 3, 2018
Related Publication 20210210184A1 · Jul 8, 2021
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