International Business Machines Corporation

Tyler Shore Baldwin

Yufan Guo

Tanveer Fathima Syeda-Mahmood

Yunyao Li

David James Beymer

Vandana Mukherjee

Shivakumar Vaithyanathan

Ioana Roxana Stanoi

Benny Kimelfeld

Bogdan Alexe

Ching-Tien (Howard) Ho

Congle Zhang

A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Detecting discrepancies between clinical notes and administrative records

Mechanisms are provided to implement a natural language request processing engine (NLRPE). The NRLPE performs natural language processing on a portion of unstructured text in an electronic data structure to generate textual characteristics of the portion of unstructured text. The NRLPE annotates at least one phrase in the portion of unstructured text at least by linking the at least one phrase to one or more concepts specified in at least one ontological data structure based on the textual characteristics of the portion of unstructured text. The NRLPE generates a model of the portion of unstructured text based on the one or more concepts linked to the at least one phrase. The NRLPE processes a request for information specifying a concept of interest based on the model of the portion of unstructured text by retrieving the at least one phrase or the at least one merged phrase as a response.

Classifying medically relevant phrases from a patient's electronic medical records into relevant categories

Mechanisms are provided to implement a natural language request processing engine (NLRPE). The NRLPE performs natural language processing on a portion of unstructured text in an electronic data structure to generate textual characteristics of the portion of unstructured text. The NRLPE annotates at least one phrase in the portion of unstructured text at least by linking the at least one phrase to one or more concepts specified in at least one ontological data structure based on the textual characteristics of the portion of unstructured text. The NRLPE generates a model of the portion of unstructured text based on the one or more concepts linked to the at least one phrase. The NRLPE processes a request for information specifying a concept of interest based on the model of the portion of unstructured text by retrieving the at least one phrase as a response.

Identifying medically relevant phrases from a patient's electronic medical records

Embodiments of the invention relate to ambiguity detection. In one embodiment, an object and a topical domain associated with the object are obtained. In this embodiment, the object includes at least one term. At least one of a plurality of information sources is analyzed based on the at least one term and the topical domain. A determination is made that object is one of ambiguous and unambiguous based on analyzing at least one of the plurality of information sources.

Knowledge-rich automatic term disambiguation

Embodiments of the present invention relate to a customizable text normalization framework providing for domain adaptability through modular replacement generators. In one embodiment, a method of and computer program product for text normalization are provided. An input sequence comprising a plurality of tokens is received. A plurality of generators is applied to the input sequence to generate a set of candidate replacements of the tokens of the sequence. A plurality of subsets of the set of candidate replacements is determined such that the candidate replacements of each subset are syntactically consistent. A probability is determined for each of the subsets. A subset of the plurality of subsets having the highest probability is selected. Each candidate replacement of the selected subset is applied to the input sequence to generate an output sequence. The output sequence is outputted.

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San Jose, CA, United States of America

Tyler Shore Baldwin