Information model query

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Background

Information model query language is designed to query both the information model and any health records that map to an IM data model.

As an RDF Graph knowledge base the information model could (and can) be directly queried using SPARQL. The IM also holds text data which can be queried directly using open Search or elastic.

However, as Health records are likely to be stored as relational, or at least SQL compatible data bases, querying health records that are aligned with the model will require SQL to query them.

Consider the following issues with SQL/SPARQL:

  • Directly authoring SQL and SPARQL languages require a high degree of skill and health query in particular needs heavily nested subqueries , including some of the more advanced techniques such as correlated query or window functions.
  • Translating a user oriented intuitive query builder into SQL or SPARQL directly and in reverse is very difficult. Most query applications use an intermediate language from which the queries are then generated. Examples include GraphQL or Power BI DAX and M.
  • Enabling direct query via SPARQL end points or SQL APIs can result in crippling performance problems.

Consequently the IM provides a pragmatic Query domain specific language (DSL) to help bridge the gap between a plain language representation and the run time query. This DSL can be used to exchange query definitions across multiple instances.

The IM also provides reference software showing how SQL or SPARQL or OpenSearch Query can be generated from the DSL and how a plain language or diagrammatic interpretation can be produced from the DSL. The reference software also shows the converse i.e. the generation of the DSL from plain language.

It is not expected that the language is understood by clinicians, or even those familiar with SQL. Instead the purpose of the language is to enable interoperability of query definition and use across standard REST APIs. However, the logic of a query definition precisely follows the logic of a plain language description of the output required and the criteria to be applied.

The language is designed to meet the following requirements

Query language requirements

Requirement 1 - Should support the vast majority of query patterns for defining and producing data sets or patient profiles that are needed in the real world.

Requirement 2 - Should enable mapping to SQL via simple type-table, property- field maps, for health data held in relational forms, as long as the health data content conforms to an IM data model

Requirement 3 - Should enable mapping to SPARQL directly for querying the IM itself

Requirement 4 - Should enable mapping directly from and to Expression constraint language (ECL) for searching and set definitions.

Requirement 5 - Should enable a technical query to be built as Java Script or POJO objects avoiding the need for a language specific parser.

Requirement 6- Should embed inference statements such as subtype, super type, or set inclusion as part of the query definition, thus avoiding the need for explicit modelling of the complex logic in the query itself

Requirement 7 - Should support object result format as well as relational format i.e. nested json object results as well as flat table results.

Requirement 8 - Should follow a logical plain language definition of the output and criteria to be applied but there is no requirement for the language itself to be understandable by non technical users.

Requirement 9 - Should support at least one plain language and at least one diagrammatic representation of the query that can be understood by lay user.

In other words the requirement is not only to create an intermediate query model but to include a translator to and from an understandable language or diagram.


Language overview

The language follows the familiar pattern of most query languages with constraints. As well as incorporating the core concepts of SQL and SPARQL it includes the nested structure approach as used by GRAPHQL.

The syntax uses json and therefore is very verbose compared with a succinct language. In return for verbosity there is less ambiguity and conforms to a standard object representation.

A query consists of the following main clauses

Query : Includes the iri, name, description, result format, use of prefixes, the main entity, Select clause and sub-select clause (where a query produces many column groups)

Select : Equivalent to SQL and SPARQL SELECT with GraphQL nesting. Includes a list of properties and aliases as well as any nested properties i.e. select property , select and a match clause and any ordering or limit

Match : Equivalent to SQL FROM/WHERE and SPARQL WHERE/FILTER. Defines the graph patterns filters or functions including ordering and limit followed by a test i.e. subquery.

Order (limit) : used in both select and match, Orders by a field and optionally limits return to a number

Example query for age over 65

Query to calculate whether a person is 65 years or over

{
  "@id" : "http://endhealth.info/im#Q_RegisteredGMS",
  "name" : "Patients registered for GMS services on the reference date",
  "description" : "For any registration period,a registration start date before the reference date and no end date,or an end date after the reference date.",
  "mainEntity" : {
    "@id" : "http://endhealth.info/im#Person"
  },
  "select" : {
    "entityType" : {
      "@id" : "http://endhealth.info/im#Person",
      "name" : "Person"
    },
    "match" : {
      "property" : {
        "@id" : "http://endhealth.info/im#isSubjectOf",
        "name" : "has GP registration"
      },
      "match" : {
        "entityType" : {
          "@id" : "http://endhealth.info/im#GPRegistration"
        },
        "and" : [ {
          "name" : "patient type is regular GMS Patient",
          "property" : {
            "@id" : "http://endhealth.info/im#gpPatientType"
          },
          "isConcept" : [ {
            "@id" : "http://endhealth.info/im#2751000252106",
            "name" : "Regular GMS patient"
          } ]
        }, {
          "name" : "start of registration is before the reference date",
          "property" : {
            "@id" : "http://endhealth.info/im#effectiveDate"
          },
          "value" : {
            "comparison" : "LESS_THAN_OR_EQUAL",
            "valueData" : "$ReferenceDate"
          }
        } ],
        "or" : [ {
          "name" : "the registration has not ended ",
          "notExist" : true,
          "property" : {
            "@id" : "http://endhealth.info/im#endDate"
          }
        }, {
          "name" : "the end of registration is after the reference date",
          "property" : {
            "@id" : "http://endhealth.info/im#endDate"
          },
          "value" : {
            "comparison" : "GREATER_THAN",
            "valueData" : "$ReferenceDate"
          }
        } ]
      }
    }
  }
}

The above query in verbose language says "select the full name of persons whose property "age" in "units" of "years is greater or equal to 65"

More complex example for patients registered as GMS regular patients on the reference date of the query

Query to calculate whether a patient is registered with a general practice on the reference date

{
  "@id" : "http://endhealth.info/im#Q_RegisteredGMS",
  "name" : "Patients registered for GMS services on the reference date",
  "description" : "For any registration period,a registration start date before the reference date and no end date,or an end date after the reference date.",
  "mainEntity" : {
    "@id" : "http://endhealth.info/im#Person"
  },
  "select" : {
    "entityType" : {
      "@id" : "http://endhealth.info/im#Person",
      "name" : "Person"
    },
    "match" : {
      "property" : {
        "@id" : "http://endhealth.info/im#isSubjectOf",
        "name" : "has GP registration"
      },
      "match" : {
        "entityType" : {
          "@id" : "http://endhealth.info/im#GPRegistration"
        },
        "and" : [ {
          "name" : "patient type is regular GMS Patient",
          "property" : {
            "@id" : "http://endhealth.info/im#gpPatientType"
          },
          "isConcept" : [ {
            "@id" : "http://endhealth.info/im#2751000252106",
            "name" : "Regular GMS patient"
          } ]
        }, {
          "name" : "start of registration is before the reference date",
          "property" : {
            "@id" : "http://endhealth.info/im#effectiveDate"
          },
          "value" : {
            "comparison" : "LESS_THAN_OR_EQUAL",
            "valueData" : "$ReferenceDate"
          }
        } ],
        "or" : [ {
          "name" : "the registration has not ended ",
          "notExist" : true,
          "property" : {
            "@id" : "http://endhealth.info/im#endDate"
          }
        }, {
          "name" : "the end of registration is after the reference date",
          "property" : {
            "@id" : "http://endhealth.info/im#endDate"
          },
          "value" : {
            "comparison" : "GREATER_THAN",
            "valueData" : "$ReferenceDate"
          }
        } ]
      }
    }
  }
}

The verbose logical description of this is to select patients who are subjects of GP registration entries and there is an entry for registration of a regular patient before the reference date and that either there is no end date for the registration (i.e. the patient is still registered) or the end date is after the reference date).

Boolean clauses and functions

The language supports unlimited nesting of Boolean clauses such as AND (all must be true), OR (at least one must be true) and OPTIONAL (may be present but if present the subsequent clause must be true) as well as negation. Booleans can apply at the level of the entity (equivalent to a JOIN in SQL) at the level of a property (NULL or outer join) or at the level of the value.

There are two patterns of functions.

  1. Function properties. These are properties in the data model whose values are generated by a function. in this case the query simply passes in the parameters to the function that are user definable. For Example "age" is modelled as a property with a function that calculates the age at a reference date but the query definition may pass in the age units.
  2. Functions operating on values. Typical example would be a time difference function between two values from the same or other query.

The language supports functions which are themselves queries

Inheritance and sets

The language supports the labelling of entities with the inclusion of subtypes, the inclusion of supertypes and the inclusion of the concept in a set. In effect this means that subsumption queries are easily performed but also there is the ability to query upward to include supertypes.

For example, when querying for allowable properties of a snomed-ct concept when creating an expression of set definition, the Snomed MRCM is examined to find the properties that have the supertypes of concepts as their domain and to return properties and their sub properties.

ECL example


<<39330711000001103          # is a Covid vaccine
OR                                            #or (
<<10363601000001109:          # is a uk product
                                                                      #and
     <<s10362601000001103 = 10362601000001103} }      #has vmp Covd vaccine)



































Grammar

This is the section on grammar