PREDICTIVE SYSTEM FOR GENERATING CLINICAL QUERIES
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predictive system that obtains and processes data describing terms for different medical concepts to generate commands from a user query. An entity module of the system determines whether a term describes a medical entity associated with a healthcare condition affecting an individual. When the term describes the medical entity an encoding module links the medical entity with a specified category based on an encoding scheme. The system receives the user query. A parsing engine of the system uses the received query to generate a machine-readable command by parsing the query against terms that describe the medical entity and based on the encoding scheme for linking the medical entity to the specified category. The system uses the command to query different databases to obtain data for generating a response to the received query.
1 . (canceled)
2 . A computer system-implemented method comprising:
encoding, using one or more machine learning models, one or more terms to a level in an encoding scheme of a category associated with the one or more terms, wherein each machine learning model is associated with a particular level in the encoding scheme;
in response to receiving a first query, generating a second query at least by parsing the first query using the encoding scheme; and
providing a reply to the first query in response to querying one or more databases using the generated second query.
3 . The computer system-implemented method of claim 2 , wherein the encoding scheme comprises a hierarchy of levels, each level of the hierarchy of levels reflects a sub-category of the category.
4 . The computer system-implemented method of claim 2 , wherein encoding the one or more terms to the level in the encoding scheme of the category associated with the one or more terms further comprises:
for each term of the one or more terms:
determining, using a model, an entity for a term;
providing the entity to each of the one or more machine learning models;
in response to providing the entity to each of the one or more machine learning models, obtaining a confidence score from each of the one or more machine learning models;
selecting an output confidence score that exceeds the other confidence scores; and
encoding, using the one of the machine learning models that produced the selected output confidence score, the entity to a level in the encoding scheme of the category that is associated with the one of the machine learning model.
5 . The computer system-implemented method of claim 4 , wherein determining, using the model, the entity for the term comprises:
generating, by the model, a confidence score for each of the one or more terms that describe the entity;
comparing, by the model, the confidence score for each of the one or more terms to a threshold value; and
in response to determining that the confidence score for each of the one or more terms exceeds the threshold value, determining, by the model, the entity for the term.
6 . The computer system-implemented method of claim 4 , further comprising:
obtaining a listing of category codes for the category;
determining a match between the term from each of the one or more terms and corresponding category codes in the listing of category codes; and
linking the entity with the category based on the match between the term that describes the entity and the corresponding category codes.
7 . The computer system-implemented method of claim 6 , wherein linking the entity with the category comprises encoding the entity with corresponding category codes based on the encoding scheme for the category.
8 . The computer system-implemented method of claim 2 , wherein generating the second query at least by parsing the first query using the encoding scheme comprises:
identifying one or more terms in the first query;
for each of the one or more terms identified in the first query: identifying an entity described by the term based on the encoding scheme for linking the entity to the category; and
generating a machine readable command using each of the identified entities.
9 . The computer system-implemented method of claim 8 , wherein providing the reply to the first query in response to querying the one or more databases using the generated second query comprises:
querying the one or more databases using the generated machine readable command;
in response to querying the one or more databases, receiving one or more data elements;
generating the reply using the one or more data elements; and
providing the reply to a client device that transmitted the first query.
10 . A system comprising:
one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
encoding, using one or more machine learning models, one or more terms to a level in an encoding scheme of a category associated with the one or more terms, wherein each machine learning model is associated with a particular level in the encoding scheme;
in response to receiving a first query, generating a second query at least by parsing the first query using the encoding scheme; and
providing a reply to the first query in response to querying one or more databases using the generated second query.
11 . The system of claim 10 , wherein the encoding scheme comprises a hierarchy of levels, each level of the hierarchy of levels reflects a sub-category of the category.
12 . The system of claim 10 , wherein encoding the one or more terms to the level in the encoding scheme of the category associated with the one or more terms further comprises:
for each term of the one or more terms:
determining, using a model, an entity for a term;
providing the entity to each of the one or more machine learning models;
in response to providing the entity to each of the one or more machine learning models, obtaining a confidence score from each of the one or more machine learning models;
selecting an output confidence score that exceeds the other confidence scores; and
encoding, using the one of the machine learning models that produced the selected output confidence score, the entity to a level in the encoding scheme of the category that is associated with the one of the machine learning model.
13 . The system of claim 12 , wherein determining, using the model, the entity for the term comprises:
generating, by the model, a confidence score for each of the one or more terms that describe the entity;
comparing, by the model, the confidence score for each of the one or more terms to a threshold value; and
in response to determining that the confidence score for each of the one or more terms exceeds the threshold value, determining, by the model, the entity for the term.
14 . The system of claim 12 , further comprising:
obtaining a listing of category codes for the category;
determining a match between the term from each of the one or more terms and corresponding category codes in the listing of category codes; and
linking the entity with the category based on the match between the term that describes the entity and the corresponding category codes.
15 . The system of claim 14 , wherein linking the entity with the category comprises encoding the entity with corresponding category codes based on the encoding scheme for the category.
16 . The system of claim 10 , wherein generating the second query at least by parsing the first query using the encoding scheme comprises:
identifying one or more terms in the first query;
for each of the one or more terms identified in the first query: identifying an entity described by the term based on the encoding scheme for linking the entity to the category; and
generating a machine readable command using each of the identified entities.
17 . The system of claim 16 , wherein providing the reply to the first query in response to querying the one or more databases using the generated second query comprises:
querying the one or more databases using the generated machine readable command;
in response to querying the one or more databases, receiving one or more data elements;
generating the reply using the one or more data elements; and
providing the reply to a client device that transmitted the first query.
18 . A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:
encoding, using one or more machine learning models, one or more terms to a level in an encoding scheme of a category associated with the one or more terms, wherein each machine learning model is associated with a particular level in the encoding scheme;
in response to receiving a first query, generating a second query at least by parsing the first query using the encoding scheme; and
providing a reply to the first query in response to querying one or more databases using the generated second query.
19 . The non-transitory computer-readable medium of claim 18 , wherein the encoding scheme comprises a hierarchy of levels, each level of the hierarchy of levels reflects a sub-category of the category.
20 . The non-transitory computer-readable medium of claim 18 , wherein encoding the one or more terms to the level in the encoding scheme of the category associated with the one or more terms further comprises:
for each term of the one or more terms:
determining, using a model, an entity for a term;
providing the entity to each of the one or more machine learning models;
in response to providing the entity to each of the one or more machine learning models, obtaining a confidence score from each of the one or more machine learning models;
selecting an output confidence score that exceeds the other confidence scores; and
encoding, using the one of the machine learning models that produced the selected output confidence score, the entity to a level in the encoding scheme of the category that is associated with the one of the machine learning model.
21 . The non-transitory computer-readable medium of claim 20 , wherein determining, using the model, the entity for the term comprises:
generating, by the model, a confidence score for each of the one or more terms that describe the entity;
comparing, by the model, the confidence score for each of the one or more terms to a threshold value; and
in response to determining that the confidence score for each of the one or more terms exceeds the threshold value, determining, by the model, the entity for the term.