Researchers Submit Patent Application, “Predictive System For Generating Clinical Queries”, for Approval (USPTO 20220083605): Patent Application

2022 APR 05 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application 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 November 22, 2021, was made available online on March 17, 2022.

No assignee for this patent application has been made.

News editors obtained the following quote 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.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “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.

“These and other implementations can each optionally include one or more of the following features. For example, in some implementations, determining whether a term describes the medical entity includes: generating a confidence score based on inferences of similarity between terms described in the first set of data and the medical entity; and determining the confidence score exceeds a threshold confidence score.

“In some implementations, linking the medical entity with the category includes: 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 specified category based on the match between the term that describes the medical entity and the corresponding category codes.

“In some implementations, linking the medical entity with the category includes: encoding the medical entity with corresponding category codes based on the encoding scheme for the specified category; and the encoding scheme for the specified category is a hierarchical encoding scheme including a hierarchy of levels.

“In some implementations, encoding the medical entity with corresponding category codes includes: quantifying content including 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.

“In some implementations, the medical entity is a disease, and determining the match includes: 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.

“In some implementations, determining that the term describes the medical entity includes: 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.

“In some implementations, the medical entity is associated with a healthcare condition that affects an individual, and the medical entity includes 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 multiple medical findings that correspond to a healthcare condition of the individual.

“In some implementations, the entity specific dataset is generated based on data including at least one of: i) a predefined set of information describing multiple diseases; ii) a predefined set of information describing multiple drugs; iii) a predefined set of information describing multiple medical procedures; or iv) electronic medical data for multiple healthcare patients.

“In some implementations, obtaining the data that describes the terms relating to the multiple medical concepts includes: obtaining multiple unstructured data; and structuring the unstructured data to enable processing of the query against information in the multiple databases.

“Other implementations of this and other aspects include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices. A computing system of one or more computers or hardware circuits can be so configured by virtue of software, firmware, hardware, or a combination of them installed on the system that in operation cause the system to perform the actions. One or more computer programs can be so configured by virtue of having instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

“The subject matter described in this specification can be implemented to realize one or more of the following advantages. The described techniques provide a scalable computing system that is a fully automated end-to-end predictive solution for analyzing and parsing structured and unstructured datasets. Using the analyzing and parsing functions, a predictive model of the system is configured such that information in the datasets can be queried using a machine-readable command that is generated based on data inferences learned by the predictive model.

“The predictive system is configured to quickly and efficiently analyze multiple datasets that describe a variety of diseases and indications, drugs/treatment options, and medical procedures. For example, the efficiency of the system is evidenced by the use of three steps to generate the command: (1) extraction of entities that describe patient attributes; (2) mapping a condition to a standardized scientific entity name; and (3) interpreting relationships between different healthcare conditions, including whether the conditions are negated or not.

“Hence, the system provides a solution that specializes in recognizing and encoding medical language terms and requires little (or no) manual data curation to achieve a desired level of accuracy in the commands or machine-readable queries that are generated and processed to obtain a response to user input. The predictive system uses learning algorithms (e.g., deep learning algorithms) to determine relationships between relevant categories of information and uses the relations between the information categories to directly query medical and research databases.”

There is additional summary information. Please visit full patent to read further.”

The claims supplied by the inventors are:

“1. A computer system-implemented method comprising: obtaining, by one or more processors of the computer system, first data comprising medical terms; determining, by a predictive model of the computer system, a respective medical entity for each term of the medical terms; for each medical term: encoding based on a respective medical category for the respective medical entity, the respective medical entity with the respective medical category of a hierarchal encoding scheme; generating, by the one or more processors of the computer system, a second query based on content of a received first query, the content representative of (i) one or more of the medical terms and (ii) information about a medical entity encoded to a category of the one or more of the medical terms; querying, by the one or more processors of the computer system, one or more databases using the second query; and providing, by the one or more processors of the computer system, a reply to the first query using results from querying the one or more databases.

“2. The computer system-implemented method of claim 1, wherein determining the respective medical entity for each term of the medical terms further comprises: generating, by the predictive model of the system, a confidence score for each of the medical terms that describe a respective medical entity; comparing, by the predictive model of the system, the confidence score for each of the medical terms to a threshold value; and in response to determining that the confidence score for each of the medical terms exceeds the threshold value, determining, by the predictive model system, that the respective medical entity corresponds to the medical term.

“3. The computer system-implemented method of claim 1, comprising: encoding, by a first encoding module that is trained to encode medical entities associated with health related disease, the respective medical entity with a disease related medical category; encoding, by a second encoding module that is trained to encode medical entities associated with pharmaceutical drugs, the respective medical entity with a drug related medical category; encoding, by a third encoding module that is trained to encode medical entities associated with medical procedures, the respective medical entity with a medical procedure related medical category; and encoding, by a fourth encoding module that is trained to encode medical entities associated with genetic markers, the respective medical entity with a genetic marker related medical category.

“4. The computer system-implemented method of claim 1, wherein encoding the respective medical entity with the respective medical category of the hierarchal encoding scheme comprises: obtaining, by an encoding module representative of the respective medical category, a listing of category codes for the respective medical category; determining, by the encoding module representative of the respective medical category, a match between the medical term and one or more corresponding category codes in the listing of category codes; and linking, by the encoding module representative of the respective medical category, the medical entity with the category based on the match between the medical term that describes the medical entity and the corresponding category codes at a particular depth level of the hierarchal encoding scheme.

“5. The computer system-implemented method of claim 4, wherein encoding the respective medical entity with the respective medical category of the hierarchal encoding scheme comprises: quantifying, by the encoding module representative of the respective medical category, content comprising the medical entity to be encoded; determining, by the encoding module representative of the respective medical category, depths levels in the hierarchal encoding scheme for mapping the content; and associating, by the encoding module representative of the respective medical category, the medical entity included in the content with the corresponding category codes for a particular depth level in the hierarchy encoding scheme.

“6. The computer system-implemented method of claim 1, wherein generating the second query based on the content in the first query comprises: extracting, by the one or more processors of the computer system, one or more terms and second data in the first query, wherein the second data comprises (i) semantic attributes of the one or more terms in the first query and (ii) a sentence syntax of the one or more terms in the first query; and generating, by the one or more processors of the computer system, a machine readable commend for querying the one or more databases, the machine readable command based on the one or more terms against the encoding between the respective medical entity with the respective medical category at a particular depth level in the hierarchal encoding scheme.

“7. The computer system-implemented method of claim 1, wherein encoding the respective medical entity with the respective medical category of the hierarchal encoding scheme comprises: generating, using one or more trained neural networks, an output score for the respective medical entity for each depth level in the hierarchal encoding scheme; comparing, using the one or more trained neural networks, each output score to a threshold value; and in response to determining the output score for a particular depth level exceeds the threshold value by an amount greater than the other depth levels in the hierarchal encoding scheme, identifying, by the one or more trained neural networks, the particular depth level in the hierarchal encoding scheme for mapping the medical entity with the respective medical category.

“8. 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, by one or more processors of the system, first data comprising medical terms; determining, by a predictive model of the system, a respective medical entity for each term of the medical terms; for each medical term: encoding based on a respective medical category for the respective medical entity, the respective medical entity with the respective medical category of a hierarchal encoding scheme; generating, by the one or more processors of the system, a second query based on content of a received first query, the content representative of (i) one or more of the medical terms and (ii) information about a medical entity encoded to a category of the one or more of the medical terms; querying, by the one or more processors of the system, one or more databases using the second query; and providing, by the one or more processors of the system, a reply to the first query using results from querying the one or more databases.

“9. The system of claim 8, wherein determining the respective medical entity for each term of the medical terms further comprises: generating, by the predictive model of the system, a confidence score for each of the medical terms that describe a respective medical entity; comparing, by the predictive model of the system, the confidence score for each of the medical terms to a threshold value; and in response to determining that the confidence score for each of the medical terms exceeds the threshold value, determining, by the predictive model system, that the respective medical entity corresponds to the medical term.

“10. The system of claim 8, comprising: encoding, by a first encoding module that is trained to encode medical entities associated with health related disease, the respective medical entity with a disease related medical category; encoding, by a second encoding module that is trained to encode medical entities associated with pharmaceutical drugs, the respective medical entity with a drug related medical category; encoding, by a third encoding module that is trained to encode medical entities associated with medical procedures, the respective medical entity with a medical procedure related medical category; and encoding, by a fourth encoding module that is trained to encode medical entities associated with genetic markers, the respective medical entity with a genetic marker related medical category.

“11. The system of claim 8, wherein encoding the respective medical entity with the respective medical category of the hierarchal encoding scheme comprises: obtaining, by an encoding module representative of the respective medical category, a listing of category codes for the respective medical category; determining, by the encoding module representative of the respective medical category, a match between the medical term and one or more corresponding category codes in the listing of category codes; and linking, by the encoding module representative of the respective medical category, the medical entity with the category based on the match between the medical term that describes the medical entity and the corresponding category codes at a particular depth level of the hierarchal encoding scheme.

“12. The system of claim 11, wherein encoding the respective medical entity with the respective medical category of the hierarchal encoding scheme comprises: quantifying, by the encoding module representative of the respective medical category, content comprising the medical entity to be encoded; determining, by the encoding module representative of the respective medical category, depths levels in the hierarchal encoding scheme for mapping the content; and associating, by the encoding module representative of the respective medical category, the medical entity included in the content with the corresponding category codes for a particular depth level in the hierarchy encoding scheme.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent application, see: Arbona, Joaquin Palancar; Duishoev, Nurlanbek; Glass, Lucas; Morgan, Kristy; Sakhrani, Shyam. Predictive System For Generating Clinical Queries. Filed November 22, 2021 and posted March 17, 2022. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220220083605%22.PGNR.&OS=DN/20220083605&RS=DN/20220083605

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