Mapping working example: Difference between revisions
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Manually mapping hundreds of fields and values can be extremely laborious and prone to error. | Manually mapping hundreds of fields and values can be extremely laborious and prone to error. | ||
Mapping suggestion algorithms offer machine | Mapping suggestion algorithms offer machine assisted hints to manually selected mappings. | ||
The algorithms work by starting with a context provided by the application, and via a series of iterations, narrow down the options to a small number. The algorithms are further tuned for specific patterns found in some source fields and values, and perhaps some authoring conventions when the target concepts were created. | The algorithms work by starting with a context provided by the application, and via a series of iterations, narrow down the options to a small number. The algorithms are further tuned for specific patterns found in some source fields and values, and perhaps some authoring conventions when the target concepts were created. | ||
In some cases, confidence levels are high enough to assume a single match (equivalent class axiom) and in this case it would be expected that a user validated a mapping once matched | |||
=Table and field hints= | =Table and field hints= | ||
Take the following working example | |||
<syntaxhighlight lang="JSON"> | |||
{ | |||
"InformationModel": { | |||
"Mapping": { | |||
"Source": { | |||
"Provider": "Barts", | |||
"System": "CernerMillenium", | |||
"Context": { | |||
"id": 1, | |||
"Table": "AdmittedPatientCare", | |||
"Field": "PatientClassificationCode", | |||
"Value": 1}}}}} | |||
</syntaxhighlight> |
Revision as of 11:44, 26 May 2020
Manually mapping hundreds of fields and values can be extremely laborious and prone to error.
Mapping suggestion algorithms offer machine assisted hints to manually selected mappings.
The algorithms work by starting with a context provided by the application, and via a series of iterations, narrow down the options to a small number. The algorithms are further tuned for specific patterns found in some source fields and values, and perhaps some authoring conventions when the target concepts were created.
In some cases, confidence levels are high enough to assume a single match (equivalent class axiom) and in this case it would be expected that a user validated a mapping once matched
Table and field hints
Take the following working example
{
"InformationModel": {
"Mapping": {
"Source": {
"Provider": "Barts",
"System": "CernerMillenium",
"Context": {
"id": 1,
"Table": "AdmittedPatientCare",
"Field": "PatientClassificationCode",
"Value": 1}}}}}