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      A Novel Web Application for Rapidly Searching the Diagnostic Case Archive

      Journal of Pathology Informatics

      Wolters Kluwer - Medknow

      Education, pathology reports, python, text search, web application

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          Abstract

          Academic pathologists must have the ability to search their institution's archive of diagnostic case data. This ability is foundational for research, education, and other academic activities. However, the built-in search functions of commercial laboratory information systems are not always optimized for this activity, leading to delays between an initial search request, and eventual results delivery. To solve this problem, a novel web-based search platform was developed, named Pathtools, which allows our staff and trainees to directly and rapidly search our diagnostic case archive. Pathtools was built with open-source components and features a web-based user-interface. Pathtools uses an SQL database which was populated with anatomic pathology case data going back to 1980, and contains 4.2 million cases (as of July 31, 2020). Pathtools has two major modes of operation, “Preview Mode” and “Research Mode.” Since deployment in February of 2019, Pathtools carried out 33,817 searches in Preview Mode, averaging 0.72 s (standard deviation = 1.7) between search submission, and on-screen display of search results. In Research Mode, Pathtools has also been used to produce data sets for research activity, providing the data used in many abstracts and manuscripts our investigators submitted recently. Interestingly, 75% of search activity is from trainees during their preview time. In a survey of residents and fellows, 83% used Pathtools during the majority of their preview sessions, demonstrating an important role for this resource in trainee education. In conclusion, a web-based search tool can rapidly and securely provide search capability directly to end-users, which has augmented trainee education and research activity in our department.

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          Most cited references 8

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          Natural Language Processing Technologies in Radiology Research and Clinical Applications.

          The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications.
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            Natural Language Processing in Oncology: A Review.

            Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records.
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              Data structures for statistical computing in python

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                Author and article information

                Journal
                J Pathol Inform
                J Pathol Inform
                JPI
                Journal of Pathology Informatics
                Wolters Kluwer - Medknow (India )
                2229-5089
                2153-3539
                2020
                24 December 2020
                : 11
                Affiliations
                Department of Anatomic Pathology, Cleveland Clinic, Cleveland, OH, USA
                Author notes
                Address for correspondence: Dr. Scott Robertson, Department of Anatomic Pathology, L25, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA. E-mail: roberts10@ 123456ccf.org
                Article
                JPI-11-39
                10.4103/jpi.jpi_43_20
                8020840
                Copyright: © 2020 Journal of Pathology Informatics

                This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

                Categories
                Technical Note

                Pathology

                web application, text search, python, pathology reports, education

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