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US 9,317,601 B2Granted
Systems, methods, and software for assessing ambiguity of medical terms
AssigneeThomson Reuters Global Resources
InventorsChristopher C. Dozier, Mark Chaudhary, Ravi Kondadadi
Life of this patent
Estimating the term…
Abstract
Some known medical terms may function as non-medical terms depending on their particular context. Accordingly, the present inventors devised systems, methods, and software that facilitate determining whether a term that is found in a medical corpus is likely to be a medical term when found in another corpus. An exemplary embodiment receives a term and computes an ambiguity score based on language models for a medical and a non-medical corpus.
Claims18
A computer-implemented method comprising:
- receiving a term;
- determining by the computer an ambiguity score for the term, wherein the ambiguity score is on ratio of a probability of the term and at least first, second and third language models of a plurality of language models, and wherein the first language model is based on a medical corpus of documents and the second language model is based on a general news corpus of documents and the third language model is based on a legal corpus of documents,
- wherein the ambiguity score for the term is determined using the function:
- S
- t
- n
- =
- λ
- 1
-
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 2
- )
- )
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 1
- )
- )
- +
- λ
- 2
-
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 3
- )
- )
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 1
- )
- )
- where Stnis the ambiguity score for term tn,λ1 is a first constant, λ2 is a second constant, P is a function of probability, M1 is the first language model, M2 is the second language model, and M3 is a third language model;
- and
- outputting by the computer the ambiguity score for the term,
- wherein the ambiguity score for the term is outputted as ranked list, with each score associated with corresponding terms.
67 more elements
A computerized system comprising:
- an input for receiving by a computer a terms;
- a processor for executing code adapted to determine by the computer an ambiguity score for the term, wherein the ambiguity score is based on a ratio of a probability of the term and at least first, second and third language models of a plurality of language models, and wherein the first language model is based on a medical corpus of documents and the second language model is based on a general news corpus of documents and the third language model is based on a legal corpus of documents,
- wherein the ambiguity score for the term is determined using the function:
- S
- t
- n
- =
- λ
- 1
-
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 2
- )
- )
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 1
- )
- )
- +
- λ
- 2
-
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 3
- )
- )
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 1
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- )
- where Stn is the ambiguity score for term tn, λ1 is a first constant, λ2 is a second constant, P is a function of probability, M1 is the first language model, M2 is the second language model, and M3 is a third language model, and
- means for outputting by the computer the ambiguity score for the term,
- wherein the ambiguity score for the term is outputted as ranked list, with each score associated with corresponding terms.
66 more elements
A non-transitory computer-readable medium comprising:
- a code set configured receive by a computer a term;
- a code set configured determined by the computer an ambiguity score for the term, wherein the ambiguity score is based on a ratio of a probability of the term and at least first, second and third language models of a plurality of language models, and wherein the first language model is based on a medical corpus of documents and the second language model is based on a general news corpus of documents and the third language model is based on a legal corpus of documents,
- wherein the ambiguity score for the term is determined using the function:
- S
- t
- n
- =
- λ
- 1
-
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 2
- )
- )
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 1
- )
- )
- +
- λ
- 2
-
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 3
- )
- )
- log
-
- (
- P
-
- (
- t
- n
- |
- M
- 1
- )
- )
- where Stn the ambiguity score for term tn, λ1 is a first constant, λ2 is a second constant, P is a function of probability, M1 is the first language model, M2 is the second language odel, and M3 is a third language model;
- and
- a code set configured output by the computer the ambiguity score for the term,
- wherein the ambiguity score for the term is outputted as ranked list, with each score associated with corresponding terms.
67 more elements
Dependencies are inferred from the claim text.