The study of mathematical logic led directly to Alan Turing 's theory of computationwhich suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church—Turing thesis. Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation
Male The most simple deterministic record linkage strategy would be to pick a single identifier that is assumed to be uniquely identifying, say SSN, and declare that records sharing the same value identify the same person while records not sharing the same value identify different people.
While A1, A2, and B2 appear to represent the same entity, B2 would not be included into the match because it is missing a value for SSN.
Handling exceptions such as missing identifiers involves the creation of additional record linkage rules. One such rule in the case of missing SSN might be to compare name, date of birth, sex, and ZIP code with other records in hopes of finding a match.
Running names through a phonetic algorithm such as SoundexNYSIISor metaphonecan help to resolve these types of problems though it may still stumble over surname changes as the result of marriage or divorcebut then B2 would be matched only with A1 since the ZIP code in A2 is different.
Thus, another rule would need to be created to determine whether differences in particular identifiers are acceptable such as ZIP code and which are not such as date of birth.
As this example demonstrates, even a small decrease in data quality or small increase in the complexity of the data can result in a very large increase in the number of rules necessary to link records properly. Eventually, these linkage rules will become too numerous and interrelated to build without the aid of specialized software tools.
In addition, linkage rules are often specific to the nature of the data sets they are designed to link together. One study was able to link the Social Security Death Master File with two hospital registries from the Midwestern United States using SSN, NYSIIS-encoded first name, birth month, and sex, but these rules may not work as well with data sets from other geographic regions or with data collected on younger populations.
New data that exhibit different characteristics than was initially expected could require a complete rebuilding of the record linkage rule set, which could be a very time-consuming and expensive endeavor.
Probabilistic record linkage[ edit ] Probabilistic record linkage, sometimes called fuzzy matching also probabilistic merging or fuzzy merging in the context of merging of databasestakes a different approach to the record linkage problem by taking into account a wider range of potential identifiers, computing weights for each identifier based on its estimated ability to correctly identify a match or a non-match, and using these weights to calculate the probability that two given records refer to the same entity.
Record pairs with probabilities above a certain threshold are considered to be matches, while pairs with probabilities below another threshold are considered to be non-matches; pairs that fall between these two thresholds are considered to be "possible matches" and can be dealt with accordingly e.
Whereas deterministic record linkage requires a series of potentially complex rules to be programmed ahead of time, probabilistic record linkage methods can be "trained" to perform well with much less human intervention. The u probability is the probability that an identifier in two non-matching records will agree purely by chance.
The m probability is the probability that an identifier in matching pairs will agree or be sufficiently similar, such as strings with high Jaro-Winkler distance or low Levenshtein distance.
This value would be 1.
This estimation may be done based on prior knowledge of the data sets, by manually identifying a large number of matching and non-matching pairs to "train" the probabilistic record linkage algorithm, or by iteratively running the algorithm to obtain closer estimations of the m probability.
If a value of 0.Charu Aggarwal. Biography Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. Type or paste a DOI name into the text box. Click Go. Your browser will take you to a Web page (URL) associated with that DOI name.
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Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Table Extraction Using Conditional Random Fields David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft Center for Intelligent Information Retrieval.
Background. With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research.