A method for linking records (related to an entity) from separate databases may include extracting a first record from a first database as a first vector, extracting a second record from a second database as a second vector, generating first and second sub-vectors for the first and second vectors, where each sub-vector includes quality features from the respective vector, pre-processing the first and second sub-vectors using domain knowledge, calculating a distance assessment classifier based on the first and second sub-vectors, and determining whether the distance represented by the distance assessment classifier is greater than a threshold. If the distance is greater than the threshold, the records may be linked; if not, the method extracts additional records and repeats after generating first and second sub-vectors until the distance is greater than the threshold. A system for linking records is also disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for linking records from separate databases, the records related to an entity, the method comprising: extracting a first record from a first database as a first vector; extracting a second record from a second database as a second vector; generating first and second sub-vectors for the first and second vectors, each sub-vector comprising quality features from the respective vector, pre-processing the first and second sub-vectors using domain knowledge; calculating a distance assessment classifier based on the first and second sub-vectors; determining whether the distance represented by the distance assessment classifier is greater than a threshold; if the distance is greater than the threshold, linking the records; and if the distance is not greater than the threshold, extracting additional records and returning to the generating first and second sub-vector operation until the distance is greater than the threshold. 2. The method of claim 1, wherein the pre-processing comprises cleaning or normalizing extracted values of the sub-vectors. 3. The method of claim 2, wherein the cleaning comprises at least one of making all letters uppercase, removing trailing spaces, removing hyphens or dashes, converting literals to numbers, and converting numbers to literals. 4. The method of claim 1, wherein the quality features comprise a social security number. 5. The method of claim 1, wherein the quality features comprise a compound used in a clinical setting. 6. The method of claim 1, wherein the quality features comprise an email address. 7. The method of claim 1, wherein the distance represented by the distance assessment classifier may be calculated as a sum of atomic probabilistic distance metrics. 8. The method of claim 7, wherein each atomic probabilistic distance metric is an algorithmic weighted quality feature. 9. The method of claim 1, further comprising eliminating unnecessary comparisons of distance assessment classifiers. 10. The method of claim 1, further comprising de-duplicating the distance assessment classifiers wherein those classifiers with close probabilities are not recorded as separate output entities. 11. A system for linking records from separate clinical trial databases, comprising: an enterprise information integration subsystem for integrating several database schemas into one federated schema; a data cleaner for parsing and cleaning up the data; a data normalizer and labeler for normalizing and labeling the cleansed data by standardizing lexical variations and ontological concepts; a feature vector builder for building features that map data in a finite dimensional space for comparison and separation; an entity classifier configured to resolve entities in the finite dimensional space using probabilistic matching; an entity clusterer for grouping data based on similarity in the finite dimensional space built by the feature vector builder; an application programming interface to interact with the data produced by the entity classifier and entity clusterer; and a linked database having a harmonized schema for presenting the resolved records. 12. The system of claim 11, wherein the enterprise information integration subsystem provides a database connectivity programming interface to one or more databases. 13. The system of claim 11, wherein the enterprise information integration subsystem supports automatic optimization of SQL queries. 14. The system of claim 11, wherein the application programming interface (API) is a REST-ful API. 15. The system of claim 11, wherein the probabilistic matching comprises calculating a distance between two records. 16. A method for automatically selecting parameters for use in linking database records, comprising: calculating a cutoff threshold by executing an entity resolution algorithm at least two times and observing the number of matches produced; determining, using domain knowledge, components of each record to be included in a sub-vector; determining atomic comparators; calculating a field quality value for each component of the sub-vector; selecting vectors from input data to generate training sets based on distances between vectors calculated as a sum of distances between components of sub-vectors multiplied by the field quality values; sampling the selected vectors against the training sets; and classifying the vectors binarily using the training sets. 17. The method of claim 16, wherein the entity resolution algorithm comprises the Jaro-Winkler distance metric. 18. The method of claim 16, wherein the entity resolution algorithm is selected from a genetic, harmony, or machine-learning algorithm. 19. The method of claim 16, wherein the cutoff threshold may be calculated when the number of matches in an algorithmic curve is stable in relation to changes in the cutoff threshold value. 20. The method of claim 16, wherein determining atomic comparators and calculating field quality values comprises finding a multi-parametric area in an algorithmic curve in which the number of matches does not substantially change in relation to changes in the field quality values and atomic comparators.