Biomedical subjects

Christopher G Chute

Publications and source records attributed to Christopher G Chute.

21 records · Page 2Linked to original sources

PubMed2002

The horizontal and vertical nature of patient phenotype retrieval: new directions for clinical text processing.

The author reviews the historical problem of identifying appropriate patients for retrieval from a clinical repository of patient records, compares the competing features of document classification and natural language processing, and proposes an alternative approach. The alternative approach 1) codes inquiries in an ontology to lend a vertical axis to retrieval knowledge instead of coding the target body of notes, 2) invokes natural language indexing and lexical normalizations on the corpus of notes that is scalable and tractable, and 3) leverages thesauri of word-level synonyms and near-synonyms to expand term searches "horizontally" around the concept spaces drawn from the ontology in which the queries were "coded."

Abstracting and Indexing↗

PubMed2002

Maximum entropy modeling for mining patient medication status from free text.

Using a classification scheme of patient medication status we sought to recognize and categorize medications mentioned in the unrestricted text of clinical documents generated in clinical practice. The categories refer to the patient's status with respect to the medication such as discontinuation, start or initiation, and continuation of a given medication. This categorization is performed with a machine learning technique, Maximum Entropy (ME), that is well suited to incorporating heterogeneous sources of information necessary for classifying patient's medication status. We use hand labeled training data to generate ME models and test 5 different training feature sets. Our results show that the most optimal feature set includes a combination of the following: two words preceding and following the mention of the drug, the subject of the sentence in which the drug mention occurs, the 2 words following the subject, and a binary feature vector of lexicalized semantic cues indicative of medication status or its change. The average predictive power of a model trained on these features is approximately 89%.

Artificial Intelligence↗

PubMedDate not supplied

A term extraction tool for expanding content in the domain of functioning, disability, and health: proof of concept.

Among the challenges in developing terminology systems is providing complete content coverage of specialized subject fields. This paper reports on a term extraction tool designed for the development and expansion of terminology systems concerned with functioning, disability, and health. Content relevant to this domain is the emphasis of the foci and targets of many nursing terminologies. We extend previously published term extraction algorithms by applying two filters. The first filter is based on the raw frequency of the content words in the lexical string under consideration. The second filter applies the notion of a complete syntactic node to discover relevant noun or verb phrases. While we report on a limited corpus (30,607 words comprising 4103 terms from 60 dismissal note summaries), the recall, precision, and F-measures we observed are encouraging and suggest continued development and testing of the tool is merited.

Algorithms↗