Automatic indexing of pathology data

Dunham, George S.; Pacak, Milos; Pratt, Arnold W.

doi:10.1002/asi.4630290207

Cited by 32 publications

(13 citation statements)

References 9 publications

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“…From 2004, we notice a sharp increase in papers about extracting structured information from pathology reports. There seems to be a decrease in papers about autocoding reports, most of these being published during the first period of investigation, 1994–1998 (and actually before) 5 6…”

Section: Resultsmentioning

confidence: 99%

“…As far back as in 1966,5 there have been attempts to structure and encode pathology data,6 which are widely recognised as essential for fast and reliable access to diagnostic information, especially for cancer registries 7–9. Medical practice today increasingly requires precise data on numerical parameters, such as information about tumour grade and size, and distance to resection margin.…”

Section: Introductionmentioning

confidence: 99%

“…Early attempts to extract encoded data from pathology reports consisted of automated retrieval of terms occurring in these reports and representing these as SNOP/SNOMED codes 6 11 12. Later, more advanced approaches using several natural language processing (NLP) techniques were used, which also provided for extraction of numerical data.…”

Section: Introductionmentioning

confidence: 99%

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Natural language processing in pathology: a scoping review

et al. 2016

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Natural language processing in pathology: a scoping review

et al. 2016

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“…in 1978. [7] The potential difficulties in extracting information from surgical pathology reports were highlighted by Liu et al ., who concluded that some variables are better “targets” for extraction than others. Staging and grading of cancer appeared to be particularly difficult to auto annotate.…”

Section: Discussionmentioning

confidence: 99%

The feasibility of using natural language processing to extract clinical information from breast pathology reports

Buckley

Coopey

Sharko

et al. 2012

Journal of Pathology Informatics

100

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Objective:The opportunity to integrate clinical decision support systems into clinical practice is limited due to the lack of structured, machine readable data in the current format of the electronic health record. Natural language processing has been designed to convert free text into machine readable data. The aim of the current study was to ascertain the feasibility of using natural language processing to extract clinical information from >76,000 breast pathology reports.Approach and Procedure:Breast pathology reports from three institutions were analyzed using natural language processing software (Clearforest, Waltham, MA) to extract information on a variety of pathologic diagnoses of interest. Data tables were created from the extracted information according to date of surgery, side of surgery, and medical record number. The variety of ways in which each diagnosis could be represented was recorded, as a means of demonstrating the complexity of machine interpretation of free text.Results:There was widespread variation in how pathologists reported common pathologic diagnoses. We report, for example, 124 ways of saying invasive ductal carcinoma and 95 ways of saying invasive lobular carcinoma. There were >4000 ways of saying invasive ductal carcinoma was not present. Natural language processor sensitivity and specificity were 99.1% and 96.5% when compared to expert human coders.Conclusion:We have demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task.

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“…• controlled indexing on multi-word terms must consider the numerous forms of term variations (Dunham, Pacak, and Pratt, 1978;Sparck Jones and Tait, 1984;Jacquemin, 1996).…”

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confidence: 99%

Expansion of multi-word terms for indexing and retrieval using morphology and syntax

Jacquemin

Klavans

Tzoukermann³

1997

Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics -

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A system for the automatic production of controlled index terms is presented using linguistically-motivated techniques. This includes a finite-state part of speech tagger, a derivational morphological processor for analysis and generation, and a unificationbased shallow-level parser using transformational rules over syntactic patterns. The contribution of this research is the successful combination of parsing over a seed term list coupled with derivational morphology to achieve greater coverage of multi-word terms for indexing and retrieval. Final results are evaluated for precision and recall, and implications for indexing and retrieval are discussed. MotivationTerms are known to be excellent descriptors of the informational content of textual documents (Srinivasan, 1996), but they are subject to numerous linguistic variations. Terms cannot be retrieved properly with coarse text simplification techniques (e.g. stemming); their identification requires precise and efficient NLP techniques. We have developed a domain independent system for automatic term recognition from unrestricted text. The system presented in this paper takes as input a list of controlled terms and a corpus; it detects and marks occurrences of term We would like to thank the NLP Group of Columbia University, Bell Laboratories -Lucent Technologies, and the Institut Universitaire de Technologie de Nantes for their support of the exchange visitor program for the first author. We also thank the Institut de l'Information Scientifique et Technique (INIST-CNRS) for providing us with the agricultural corpus and the associated term list, and Didier Bourigault for providing us with terms extracted from the newspaper corpus through LEXTER (Bourigault, 1993).variants within the corpus. The system takes as input a precompiled (automatically or manually) term list, and transforms it dynamically into a more complete term list by adding automatically generated variants. This method extends the limits of term extraction as currently practiced in the IR community: it takes into account multiple morphological and syntactic ways linguistic concepts are expressed within language. Our approach is a unique hybrid in allowing the use of manually produced precompiled data as input, combined with fully automatic computational methods for generating term expansions. Our results indicate that we can expand term variations at least 30% within a scientific corpus. 2Background and Introduction NLP techniques have been applied to extraction of information from corpora for tasks such as free indexing (extraction of descriptors from corpora), (Metzler and Haas, 1989;Schwarz, 1990;Sheridan and Smeaton, 1992;Strzalkowski, 1996), term acquisition (Smadja and McKeown, 1991;Bourigault, 1993;Justeson and Katz, 1995; Dallle, 1996), or extraction of lin9uistic information e.g. support verbs (Grefenstette and Teufel, 1995), and event structure of verbs (Klavans and Chodorow, 1992). Although useful, these approaches suffer from two weaknesses which we address. First is the issue...

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Automatic indexing of pathology data

Cited by 32 publications

References 9 publications

Natural language processing in pathology: a scoping review

Natural language processing in pathology: a scoping review

The feasibility of using natural language processing to extract clinical information from breast pathology reports

Expansion of multi-word terms for indexing and retrieval using morphology and syntax

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