Patent Issued for Predictive system for generating clinical queries (USPTO 11210346): IQVIA Inc.

2022 JAN 19 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Arbona, Joaquin Palancar (Madrid, ES), Duishoev, Nurlanbek (Frankfurt am Main, DE), Glass, Lucas (Devon, PA, US), Morgan, Kristy (Chapel Hill, NC, US), Sakhrani, Shyam (London, GB), filed on April 4, 2019, was published online on December 28, 2021, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11210346 is assigned to IQVIA Inc. (Parsippany, New Jersey, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “As part of the healthcare process, physicians or other medical care providers may perform clinical trials, programs, and other activities to evaluate subject safety and efficacy of a pharmaceutical drug or other medical treatment option. The use of health-related trial programs can help to identify novel treatment options for improving overall patient health and reducing health system costs. A clinical trial or program can be a single research study or multiple research studies that prospectively assigns human participants/subjects or groups of human subjects to one or more health-related interventions to evaluate the effects on health outcomes.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “As part of the healthcare process, physicians or other medical care providers may perform trials, programs, and other activities to evaluate the efficacy of a particular pharmaceutical drug or other medical treatment option. Conducting health-related clinical trials can help to identify medical treatment options for improving overall patient health and reducing health system costs. Clinical trials and other controlled programs are generally conducted by one or more investigators at medical facilities in different geographic locations that interact with study subjects to evaluate the efficacy of a drug treatment option. In some instances a physician for a patient can be associated with a clinical trial and the physician can refer a patient as a candidate for participation in a trial based on a diagnosed condition of the patient. An investigator, a geographic location, or both, can form an entity that executes a program.

“Based on the above context, this document describes a computing system that uses specific computing rules or instructions (e.g., a unique algorithm) to predict or generate commands based on a received user input. To generate the commands, the system is configured to train a predictive model using one or more learning algorithms (e.g., deep learning algorithms). The predictive model is used to process terms that are recognized and extracted using a natural language processor (NLP) in an entity module of the system. The predictive model can be trained to semantically understand relevant terms (e.g., medical and clinical terms) and their relations to other medical terms. Terms can be extracted from information sources such as textbooks and online resources, or from unstructured datasets such as electronic medical data for multiple healthcare patients.

“An encoding module uses one or more neural network models to encode and link the extracted terms to a particular medical entity, such as a disease entity, a drug entity, a medical procedure entity, or various other types of entities. The system leverages the predictive model’s learned inferences about the encoded medical terms to generate a command based on a received query from a user. For example, a parsing engine can automatically translate the extracted terms into a machine-readable command that is processed against a medical database to obtain an accurate response to the user query. Hence, at least one goal of the predictive system is to accurately interpret, in a manner that is computationally efficient, a user query that includes health-related information about a patient or set of patients.

“For example, the user query represents user input, to the predictive system, that specifies a list(s) of patient attributes. The query/user input can be in a human-readable format. The described techniques enable the system to quickly and efficiently generate a corresponding command in a computer-readable format. The computer-readable command is then used to query different electronic health records (EHR) to identify patients (e.g., trial subjects) that satisfy a given condition(s) based on the attributes specified in the list. For example, the command can be used to query historical medical records to derive insights and information without manual intervention (e.g., from a human-operator). The derived insights can include accurate estimation of an eligible patient population for clinical trial participation and estimation of a propensity for adverse events).

“One aspect of the subject matter described in this specification can be embodied in a computer-implemented method that includes: obtaining a first set of data including multiple terms; determining that a term of the multiple terms describes a medical entity; responsive to determining that the term describes the medical entity, linking the medical entity with a category based on an encoding scheme for the category; responsive to receiving a query, generating a machine-readable command by parsing the query against terms in the first set of data that describe the medical entity and based on the encoding scheme; using the machine-readable command to query multiple databases; obtaining a second set of data responsive to the received query when the machine-readable command is used to query the multiple databases; and providing the second set of data as an output for display at a user device.”

The claims supplied by the inventors are:

“1. A computer-implemented method performed using a system, the method comprising: obtaining data comprising a plurality of terms; determining that a term of the plurality of terms describes a medical entity; determining, by a predictive model of the system, a respective confidence score between each term of the plurality of terms and the medical entity; linking, by an encoding module that interacts with the predictive model, the medical entity with a category from categories of medical information based on an encoding scheme for the category and the confidence score associated with the term, wherein the encoding module performs the linking by employing one or more machine learning models to associate the medical entity with the category for a particular depth level in a hierarchical encoding scheme comprising a hierarchy of levels; responsive to receiving a query, generating a machine-readable command at least by parsing the query against information about the medical entity linked to the category based on the encoding scheme and the confidence score; generating a reply to the query after using the machine-readable command to query one or more databases; and providing the reply as an output for display at a user device.

“2. The method of claim 1, wherein determining whether a term describes the medical entity comprises: determining the confidence score exceeds a threshold confidence score.

“3. The method of claim 1, wherein linking the medical entity with the category comprises: obtaining a listing of category codes for the category; determining a match between the term and corresponding category codes in the listing of category codes; and linking the medical entity with the category based on the match between the term that describes the medical entity and the corresponding category codes.

“4. The method of claim 1, wherein linking the medical entity with the category comprises: encoding the medical entity with corresponding category codes based on the encoding scheme for the category; and wherein the encoding scheme for the category is the hierarchical encoding scheme comprising the hierarchy of levels.

“5. The method of claim 4, wherein encoding the medical entity with corresponding category codes includes: quantifying content comprising the medical entity to be encoded; determining depths of levels in the hierarchy of levels for mapping the content; and associating the medical entity included in the content with the corresponding category codes for a particular depth level in the hierarchy of levels.

“6. The method of claim 4, wherein the medical entity is a disease, and determining the match comprises: generating a respective match score for each level in the hierarchy of levels; and determining that the respective match score exceeds a threshold match score.

“7. The method of claim 1, wherein determining that the term describes the medical entity comprises: performing a lookup of the term against information in an entity-specific dataset; and determining that the term describes the medical entity based on a match between the term and a first entry in the entity-specific dataset.

“8. The method of claim 7, wherein the medical entity is associated with a healthcare condition that affects an individual, and wherein the medical entity comprises at least one of: one or more medical diseases; medical drugs for treating the one or more medical diseases; medical procedures associated with the one or more medical diseases; or data describing a plurality of medical findings that correspond to a healthcare condition of the individual.

“9. The method of claim 7, wherein the entity specific dataset is generated based on data including at least one of: i) a predefined set of information describing a plurality of diseases; ii) a predefined set of information describing a plurality of drugs; iii) a predefined set of information describing a plurality of medical procedures; or iv) electronic medical data for a plurality of healthcare patients.

“10. The method of claim 1, wherein obtaining the data that describes the terms relating to a plurality of medical concepts comprises: obtaining a plurality of unstructured data; and structuring the unstructured data to enable processing of the query against information in multiple databases of the system.

“11. A system, comprising: one or more processing devices; and one or more non-transitory machine-readable storage devices storing instructions that are executable by the one or more processing devices to cause performance of operations comprising: obtaining data comprising a plurality of terms; determining that a term of the plurality of terms describes a medical entity; determining, by a predictive model of the system, a respective confidence score between each term of the plurality of terms and the medical entity; linking, by an encoding module that interacts with the predictive model, the medical entity with a category from categories of medical information based on an encoding scheme for the category and the confidence score associated with the term, wherein the encoding module performs the linking by employing one or more machine learning models to associate the medical entity with the category for a particular depth level in a hierarchical encoding scheme comprising a hierarchy of levels; responsive to receiving a query, generating a machine-readable command at least by parsing the query against information about the medical entity linked to the category based on the encoding scheme and the confidence score; generating a reply to the query after using the machine-readable command to query one or more databases; and providing the reply as an output for display at a user device.

“12. The system of claim 11, wherein determining whether a term describes the medical entity comprises: determining the confidence score exceeds a threshold confidence score.

“13. The system of claim 11, wherein linking the medical entity with the category comprises: obtaining a listing of category codes for the category; determining a match between the term and corresponding category codes in the listing of category codes; and linking the medical entity with the category based on the match between the term that describes the medical entity and the corresponding category codes.

“14. The system of claim 11, wherein linking the medical entity with the category comprises: encoding the medical entity with corresponding category codes based on the encoding scheme for the category; and wherein the encoding scheme for the category is the hierarchical encoding scheme comprising the hierarchy of levels.

“15. The system of claim 14, wherein encoding the medical entity with corresponding category codes includes: quantifying content comprising the medical entity to be encoded; determining depths of levels in the hierarchy of levels for mapping the content; and associating the medical entity included in the content with corresponding category codes for a particular depth level in the hierarchy of levels.

“16. The system of claim 14, wherein the medical entity is a disease, and determining the match comprises: generating a respective match score for each level in the hierarchy of levels; and determining that the respective match score exceeds a threshold match score.

“17. The system of claim 11, wherein determining that the term describes the medical entity comprises: performing a lookup of the term against information in an entity-specific dataset; and determining that the term describes the medical entity based on a match between the term and a first entry in the entity-specific dataset.

“18. The system of claim 17, wherein the medical entity is associated with a healthcare condition that affects an individual, and wherein the medical entity comprises at least one of: one or more medical diseases; medical drugs for treating the one or more medical diseases; medical procedures associated with the one or more medical diseases; or data describing a plurality of medical findings that correspond to a healthcare condition of the individual.

“19. The system of claim 11, wherein obtaining the data that describes the terms relating to a plurality of medical concepts comprises: obtaining a plurality of unstructured data; and structuring the unstructured data to enable processing of the query against information in multiple databases of the system.

“20. One or more non-transitory machine-readable storage devices storing instructions that are executable by one or more processing devices to cause performance of operations comprising: obtaining data comprising a plurality of terms; determining that a term of the plurality of terms describes a medical entity; determining, by a predictive model of the system, a respective confidence score between each term of the plurality of terms and the medical entity; linking, by an encoding module that interacts with the predictive model, the medical entity with a category from categories of medical information based on an encoding scheme for the category and the confidence score associated with the term, wherein the encoding module performs the linking by employing one or more machine learning models to associate the medical entity with the category for a particular depth level in a hierarchical encoding scheme comprising a hierarchy of levels; responsive to receiving a query, generating a machine-readable command at least by parsing the query against information about the medical entity linked to the category based on the encoding scheme and the confidence score; generating a reply to the query after using the machine-readable command to query one or more databases; and providing the reply as an output for display at a user device.”

URL and more information on this patent, see: Arbona, Joaquin Palancar. Predictive system for generating clinical queries. U.S. Patent Number 11210346, filed April 4, 2019, and published online on December 28, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11210346.PN.&OS=PN/11210346RS=PN/11210346

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