|J Pathol Inform 2017,
Application of text information extraction system for real-time cancer case identification in an integrated healthcare organization
Fagen Xie, Janet Lee, Corrine E Munoz-Plaza, Erin E Hahn, Wansu Chen
Department of Research and Evaluation, Kaiser Permanente Southern California Medical Group, Pasadena, CA, USA
|Date of Submission||28-Jul-2017|
|Date of Acceptance||01-Nov-2017|
|Date of Web Publication||14-Dec-2017|
Dr. Fagen Xie
Department of Research and Evaluation, Kaiser Permanente Southern California Medical Group, 100 S Los Robles Ave, 2nd Floor, Pasadena CA 91101
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Surgical pathology reports (SPR) contain rich clinical diagnosis information. The text information extraction system (TIES) is an end-to-end application leveraging natural language processing technologies and focused on the processing of pathology and/or radiology reports. Methods: We deployed the TIES system and integrated SPRs into the TIES system on a daily basis at Kaiser Permanente Southern California. The breast cancer cases diagnosed in December 2013 from the Cancer Registry (CANREG) were used to validate the performance of the TIES system. The National Cancer Institute Metathesaurus (NCIM) concept terms and codes to describe breast cancer were identified through the Unified Medical Language System Terminology Service (UTS) application. The identified NCIM codes were used to search for the coded SPRs in the back-end datastore directly. The identified cases were then compared with the breast cancer patients pulled from CANREG. Results: A total of 437 breast cancer concept terms and 14 combinations of “breast” and “cancer” terms were identified from the UTS application. A total of 249 breast cancer cases diagnosed in December 2013 was pulled from CANREG. Out of these 249 cases, 241 were successfully identified by the TIES system from a total of 457 reports. The TIES system also identified an additional 277 cases that were not part of the validation sample. Out of the 277 cases, 11% were determined as highly likely to be cases after manual examinations, and 86% were in CANREG but were diagnosed in months other than December of 2013. Conclusions: The study demonstrated that the TIES system can effectively identify potential breast cancer cases in our care setting. Identified potential cases can be easily confirmed by reviewing the corresponding annotated reports through the front-end visualization interface. The TIES system is a great tool for identifying potential various cancer cases in a timely manner and on a regular basis in support of clinical research studies.
Keywords: Breast cancer, case identification, natural language processing, pathology reports, text information extraction system
|How to cite this article:|
Xie F, Lee J, Munoz-Plaza CE, Hahn EE, Chen W. Application of text information extraction system for real-time cancer case identification in an integrated healthcare organization. J Pathol Inform 2017;8:48
|How to cite this URL:|
Xie F, Lee J, Munoz-Plaza CE, Hahn EE, Chen W. Application of text information extraction system for real-time cancer case identification in an integrated healthcare organization. J Pathol Inform [serial online] 2017 [cited 2022 Jan 24];8:48. Available from: https://www.jpathinformatics.org/text.asp?2017/8/1/48/220747
| Introduction|| |
Recent innovations in computer technology have resulted in the exponential expansion of electronic information in various industries, including the rapid growth of electronic medical records (EMR) in health-care systems., EMR systems capture and store patient health information in structured and unstructured formats electronically in place of paper charts. The resulting structured healthcare data have been extensively used to support clinical operations, decision-making, and biomedical research. However, clinical narrative captured in clinician notes is the most natural and efficient way to capture communication between patients and clinicians, nuanced clinical details, and explanation for medical decision-making. These free text notes capture substantial and rich information related to patients' health conditions; however, the unstructured format can make it difficult to use this information directly in patient care management and medical research without further information processing. To resolve these challenges, over the past several decades, the field of clinical natural language processing (NLP) has been focused on developing various methods for semantic processing and analysis of these clinical texts, and can thus be applied to a variety of clinical applications. In fact, a number of clinical NLP systems have been successfully developed and implemented in a myriad of medical domains with varying focuses.,,,,,
Surgical pathology reports (SPRs) contain valuable medical information embedded in the narrative free text, including information on the gross and microscopic description of tissue. SPRs are a vital research resource, in particular, for cancer-related research.,,,,,,,,,,,, However, extracting information from SPR is generally time-consuming, laborious, costly, and requires manual processes and significant domain expertise. Alternatively, automating the application for information extraction (IE) provides a method for radically increasing the speed and scope with which this data can be accessed quickly. A series of NLP methods and applications have been developed to retrieve this type of information from pathology reports.,,,,,,,,,, For example, the text information extraction system (TIES) released by the University of Pittsburgh School of Medicine  was initiatively focused on extracting cancer information from SPR and later extended to radiology reports to support multi-center collaborative translational medical research through a federated network model. Yip et al. developed an N-gram model for concept discovery from pathology reports. The iterative online machine learning-based IE system (IDEAL-X) was introduced by Zheng et al. to incrementally process pathology reports to improve the learning model. The medical knowledge analysis tool pipeline (MedKATp), a system established by the Open Health Natural Language Processing Consortium, automatically extracts cancer-specific characteristics from unstructured clinical reports. The pathology extraction pipeline (PEP), built on MedKATp by University of California at Irvine, was also developed for extracting data elements and relationships from pathology reports. Additional examples and the details of these NLP systems were documented in the review article by Burger et al.
The TIES system leverages the Noble coder  and other well-known NLP methods and algorithms to process pathology and radiology reports. One of the advantages of the TIES system is the capacity to share de-identified data and tissue through coded SPRs among a federated research network of institutions under bundle of restrict security regulations and compliances. As of 2015, this federated TIES system had been implemented in four institutions  and accumulated >5 million coded pathology reports and 25 million radiology reports between January 2003 and January 2017. As an end-to-end application for processing both pathology and radiology reports, the TIES system is increasingly of interest to biomedical research communities and healthcare maintenance organizations to facilitate translational medical research and clinical operations., The TIES system attracted our attention due to its potential for rapid cancer case ascertainment in support of research studies. Especially, it offers detailed technical documentation and real-time online support, as well as other needed functionalities (such as deidentification and restricted secured model). On the other hand, the other systems such as MedKATp, PEP, and IDEAL-X are capable of effectively processing SPRs, but they are more focused on IE rather than case identification. Additional detail documentation and appropriate technical support are important for successful implementation of these comprehensive systems.
A majority of retrospective cancer research studies rely on local or national cancer registries (CANREGs) for cohort identification. This presents a significant challenge, especially for prospective studies such as clinical trials, because CANREG data are often delayed at least several months due to the lengthy manual process involved in collecting and coding registry data. Methods to quickly identify newly diagnosed cancer patients are critically needed to expedite prospective studies. In this paper, we will demonstrate the implementation and application of the TIES system in a large integrated health maintenance organization, Kaiser Permanente Southern California (KPSC), and the performance of the TIES application for rapid case ascertainment. Furthermore, the limitations and the caveats of the system learned through our processes will be shared to shorten the learning curves of potential future users.
| Methods|| |
Implementation of the text information extraction system
Natural language processing software
The TIES system, an open-source system formerly known as caTIES, was originally developed for the Shared Pathology Informatics Network to enable translational research within a federated network of institutions., TIES has evolved over time through many iterations with the latest available version being V5.4 at the time of our implementation. The TIES system consists of clients, services, and datastores connected and implemented under the Globus grid service architectures with a restricted regulatory model for federated data sharing (relying on Institutional Review Board (IRB) protocols and honest brokers). The unique TIES components used in our study are described below.
- Back-end Datastores: The private datastore is the recipient of data and stores the original free text reports with identifiers while the research datastore contains de-identified free text reports for the NLP Pipeline Services, which creates and stores annotations for each de-identified report
- Data preparation services: The HL7 data importer loads the identified reports with HL7 specification format into the private datastore. The de-identifier recognizes and removes the identifiers protected by the Health Insurance Portability and Accountability Act. De-identification was achieved by using the built-in MGH scrubber. The concept coding service performs a sequence of NLP processing  to produce conceptual annotations and codes based on free-text reports. The indexing service creates an index for quick access to reports based on the text and conceptual codes being searched
- Information retrieval services: The data access and integration service provide web service which interacts with data sources. The search service communicates the user entered search criteria to the TIES server
- The restrict security enforcement layers guard the resources and authorize access based on roles of users.
The detailed components, functionalities and evaluations of the TIES system have been described in the published paper of the system  and the TIES official website. The developers of the TIES system created a helpful user manual  and built a TIES community forum for technical discussions, support, user feedback, logs of issues and improvements.
Deployment of the text information extraction system
Our initial implementation of TIES version 4.01 on a Window-based server started in 2008. Due to the rapidly increasing volumes of SPRs, we adopted version 5.4 in 2016, the latest version at the time of implementation. The new version was implemented on a Linux server. After a lengthy installation and configuration process described below, the implementation was a success. Although the TIES system has the capability for integration into a federated research network, our deployment was restricted to our local network without any outside communication activities.
Installation and configuration
The required hardware was prepared and software was downloaded according to the TIES system's installation guideline. The components included: (1) configuration and installment of the Linux server and disk space allocation for data storage; (2) third-party software installed were JAVA, MySQL database server, Tomcat web server, Apache Ant, and NCI Metathesaurus; and (3) The zip executable files for TIES application, TIES Java Message Services (JMS) services for supporting parallel coding, and ActiveMQ required by the TIES JMS services. All downloaded software packages described above were first unzipped and installed into the designed folders in the Linux server. The configuration files of each installed package were then examined and adjusted to the corresponding installation settings as required.
Launching text information extraction system application, MySQL server, text information extraction system java message services, and ActiveMQ
A configured and executed script was stored at the configuration or common bin folder for each service. These scripts were submitted through a command line to launch a series of processes that brought up the TIES application, the MYSQL server, TIES JMS services, and ActiveMQ. The logs of the launching processes and the execution statues were stored in the corresponding log files. It is critical to check the log files to make sure the launching processes are error-free. The launching process could also be performed through a job scheduler.
Launching text information extraction system client
The TIES application embeds the security model of the Globus Toolkit, which uses Grid Security Infrastructure based on public key encryptions and certificates for enabling secure authentication and communication over an open or intra network. Therefore, the Globus security certificates should first be copied into the corresponding and designated folder in the user's computer, which is used to launch the TIES client. Because the TIES client is a JAVA application based on JAVA WebStart, the client machine is also required to install/update a comparable JAVA version (currently V1.7 or higher) before launching the TIES client. Using the administrator's account to login into the TIES system, we examined all administrative functionalities, such as study protocols, user access, and password. The account for an honest broker, researcher, and the preliminary user was each used to login into the TIES system to examine whether the functionalities for each role performed as designed.
Verifying the features for query construction and result visualizations
The TIES system provides two approaches for users to construct queries. The first approach is using a user-friendly interface to create queries based on concepts or texts (text strings), as shown in [Figure 1]. Users can type one or multiple keywords in the concept box, and the specific negated form in the negated box. The application shows up the corresponding concepts and National Cancer Institute Metathesaurus (NCIM) codes as the user types the searching terms. The results can be narrowed down by specifying pathology report section (s), demographics and event year. The second approach (referred to as the diagram method), demonstrated in [Figure 2], provides more flexibility and can be used to formulate complex queries consisting of multiple events with a temporal relationship. For example, the query demonstrated in [Figure 2] was to search for all patients who had breast cancer in 2013 and had a recurrent case within 1 year.
|Figure 1: User interface for query construction in the text information extraction system application|
Click here to view
|Figure 2: A demo of constructing a temporal query using a diagram method to search for patient who had breast cancer in 2013, and recurrent within one year|
Click here to view
Search results are presented nicely with a list of individual reports to the left of the results screen, and a summary by demographics and year, utilizing either a bar or pie chart format on the right side [Figure 3]. When each report listed to the left is clicked, the annotated report opens with highlighted Diagnosis, Procedure, Organ, Negated Diagnosis, Negated concept, General concept, as shown in [Figure 4] (a de-identified sample, with “xxxx” replaced the identifiable information). However, visualization of individual reports is limited to only two types of users as follows: Researcher and honest broker. Furthermore, researchers can only view the de-identified reports, in which all identified information is masked by the de-identification process in the TIES system.
|Figure 3: Searched results of example 50. Individual lists were on the left side, and statistical summaries were on right|
Click here to view
Operation of the text information extraction system at Kaiser Permanente Southern California
The CoPathPlus system, an interactive and comprehensive system of Cerner Corp, manages accessioning and handles specimens at the Kaiser Permanente national anatomic pathology laboratories. In the past few years, the system has processed more than a half million specimens annually. Pathology reports are generated for all processed surgical accessions. The final diagnosis, based on the pathologist assessment, is in the form of free text and is a mandated section of any report. All the pathology reports are loaded into the KPSC Research Data Warehouse (RDW) on a daily basis, and these reports are further loaded into the TIES system.
Architecture of the Kaiser Permanente Southern California text information extraction system and data loading
The architecture of the TIES system within the KPSC research environment is illustrated in [Figure 5]. The column to the left demonstrates the flow of data extraction from CopathPlus, to RDW, and finally to the reports formatted in the HL7 specification. The middle part consists of the TIES application server, MySQL database server and web server, whereas the right side shows client requests through the TIES's web-based client interfaces or querying directly against the back-end datastores.
|Figure 5: Architecture of the text information extraction system within the Kaiser Permanente Southern California research environment|
Click here to view
First, the historic pathology reports were retrieved and converted into HL7 specification format and transferred into the HL7 data folder in the TIES server where the HL7 importer automatically loaded these reports into the back-end datastores of the TIES system. Second, a process was set up to extract and load the pathology reports on a daily basis. To handle large daily volumes of pathology reports (~4000/day), the TIES JMS service was implemented to process the reports in parallel. Currently, the KPSC TIES system contains over 3 million pathology reports dating back to 2013. For security control, we followed our internal operational procedures and the TIES built-in security protocol to manage and monitor the authorized access and other activities through the web-based administrative interface.
Searching cases by using a flexible time window
Instead of allowing users to specify time windows on a daily basis (e.g., 1/2/2015–1/8/2015), the front-end window of installed TIES system only supports the search of time windows on an annual basis at this point. This limitation prevents the effective use of the TIES system for timely cancer identification. For example, if a user is interested in identifying breast cancer patients evidenced by pathology reports last week, he/she is not able to rely on the front-end application to do so. Therefore, we developed a query process to search against the TIES back-end datastore through a batch mode (referred to as the direct method) to support our study application described below.
Application of the text information extraction system
We conducted a validation study to examine the ability of the TIES system to identify potential breast cancer cases diagnosed in December of 2013, using the KPSC CANREG as the gold standard. This is part of a large-scale internal initiative to develop efficient processes to prospectively identify newly diagnosed cancer patients for enrolment into clinical studies. The methods and results described below are limited to the validation study only. The KPSC CANREG contains information on patients who were diagnosed or received at least part of their first course of treatment for cancer at KPSC hospitals for all reportable cancers. The diagnosis date in CANREG was based on multiple sources, including diagnostic mammograms; thus, it could be potentially different from the reported date of a pathology report. Therefore, the TIES searching time window was extended to the end of 2014 from December 2013. Because the TIES system is based on the NCIM, we first used the online the Unified Medical Language System terminology service (UTS) web application  to search all potential NCIM codes describing breast cancer and the terms described in the Buckley et al. study  to create the initial list of breast cancer terms and corresponding NCIM codes. This list was then reviewed and finalized by the study team. These selected NCIM terms and codes were then used to search against the TIES back-end datastores through the direct method described above. The search was limited to only the final diagnosis section, assuming such a section could be detected by the TIES system; otherwise, the search was applied to the entire pathology reports (the TIES system defines it as report section). Potential reasons for the failure of identifying final diagnosis sections are provided in the Discussion Section. After discovering that a majority of reports negative for breast cancer only contained negated cancer concepts (these are identified by the codes starting with the character “N”), only concepts that were not negated (the codes starting with the character “A”) were included for query extractions.
The potential breast cancer cases identified by the TIES were manually examined and compared against the CANREG data. Error analysis was performed. The study was approved by KPSC IRB.
| Results|| |
A total of 437 specific NCIM terms and codes associated with breast cancer were identified through the UTS application. These specific terms contained the combination of the anatomical location (lobular or duct), malignant disorder (carcinoma or cancer), and severity (infiltrating or metastatic). The detailed list was included in [Appendix 1]. Some positive pathology reports were not identified through breast cancer concepts because breast and cancer were mentioned separately in different sentences or places. Therefore, an additional list was created, including 14 cancer concepts that were required to occur along with the breast concept [Appendix 2].
There were a total of 249 breast cancer cases diagnosed in December 2013 based on CANREG records. Of these, 241 cases (457 pathology reports) were found by using the preidentified concept codes listed in Appendices 1 and 2. Out of the eight false negative cases, negated terms were found in the pathology reports for 3 cases. [Table 1] displays the number of the identified patients and the number of pathology reports by the concept codes used. A total of 13 preidentified concepts were found to have 10 or more reports while the “other terms” (all concepts with <10 reports combined) had 46 reports. Among the concepts for which 10 or more reports were found, the top three were “Ductal Breast Carcinoma in situ” (274 reports, and 171 patients), “Invasive Ductal Carcinoma, NOS” (197 reports and 124 patients), and “Carcinoma in situ” (156 reports and 119 patients), respectively. Nearly 56% of the diagnosis dates and pathology report dates were within 1 month, and the median of the difference of these two dates was within one and half months. The differences between the report sign-off dates and the diagnosis dates derived from the CANREG were shown in [Table 2].
|Table 1: Distribution of pathology reports identified by text information and extraction system with the predefined breast cancer concepts and codes for patients diagnosed with breast cancer in December, 2013 based on the cancer registry|
Click here to view
|Table 2: The difference between the pathology report signoff date and the breast cancer diagnosis date by the preidentified concepts and codes for patients diagnosed with breast cancer in the December, 2013 based on the cancer registry|
Click here to view
The TIES system also identified additional 277 potential cases with the report sign-off dates within December of 2013 for whom the corresponding records were not found in the subset of breast cancer patients diagnosed in December of 2013 based on the CANREG. [Table 3] shows the number of reports, as well the number of patients which fall under this category. Further research revealed that 84.8% (235) of these patients were found in the CANREG with diagnosis dates between January and November of 2013 and 1.4% (4) were found in the CANREG with diagnosis dates in 2012. Nearly 13.8% (38) of these patients were not found in the CANREG in 2012 or 2013. After manually examining the detail pathology reports, we concluded that 31 cases were highly likely to be identified by the concepts “Atypical Lobular Breast Hyperplasia,” “Ductal Breast Carcinoma in situ,” “Invasive Ductal Carcinoma, NOS.” Only seven were misclassified (false positive) due to the failure of recognizing historical or negated cases.
|Table 3: Distribution of pathology reports identified by text information and extraction system with the predefined breast cancer concepts and codes for pathologist signoff date within December, 2013 for whom the corresponding records were not found in the subset of breast cancer patients diagnosed in December of 2013 based on the cancer registry|
Click here to view
Due to the positive findings reported above, the reported algorithm was implemented in an ongoing research study to prospectively identify newly diagnosed cancer patients for recruitment.
| Discussions|| |
The TIES system is an end-to-end clinical medical NLP application which can be used to support single or multiple institutional collaborative cancer research. It has evolved over time, resulting in numerous versions since it was introduced a decade ago. The system has garnered particular attention from the NLP cancer-focused research community since the publication of the caTIES system (its previous version) in 2010 and the establishment of the TIES Cancer Research Network among four research institutions.
Validation using with patients diagnosed in December 2013 extracted from the CANREG demonstrated that the TIES system has the capability to identify 96.8% (241 of 249) of breast cancer cases. Of the eight false negative cases, one case lacked any pathology report. The misclassification of the remaining seven false negative cases was due to the following reasons: (1) The pathology reports lacked any of the study's preidentified breast cancer concepts (n = 1). (2) The pathology reports contained the concepts being searched; however, they were either negated or appeared in the other report sections such as (addendum or gross description) instead of the final diagnosis (n = 6). The TIES system also identified additional potential breast cancer cases by searching pathology reports within December 2013. However, a majority of these cases (86%) were diagnosed in 2012 or 2013. The KPSC CANREG was based on multiple sources including diagnostic mammograms, biopsy pathology reports, etc. Therefore, there was a potential time lag between the diagnosed date and biopsy reporting date, which revealed that the diagnosis date could be the earlier date, while the biopsy date was the later confirmed date. However, the median of the date difference was within one and half months.
The developers of the TIES system provide a useful web-based online forum for knowledge sharing and issue discussion, as well as system support for a limited time. However, the implementation or migration of the TIES system will continue to face challenges without a better understanding of the fundamental mechanism and framework of the system. First, trouble-shooting the errors requires a thorough understanding of the working mechanism, process flow and corresponding computer and NLP technologies. Second, the TIES default coder pipeline only runs in a single process. Thus, the performance is reasonable for small data volume but could be deteriorated as the data volume increases significantly. At KPSC, when we loaded the historic data into the TIES system, the coding process was relatively sluggish. After checking available information in the TIES online support, we realized that the TIES JMS component (optional component not included in the single download package) could be downloaded to speed up the coding process. Third, although the TIES system is an open source tool, it remains difficult to customize the design and improve the system without a deep understanding of the system and advanced technical expertise. Fourth, as the TIES system stands today, it lacks the proper components for disaster and error recovery. One error could potentially result in a full system failure, requiring a complete rebuild. In addition, although we noted that the TIES web application session can be timed out automatically after a certain period without activities, the front-end web interface lacks a user signoff/logout button. Given these identified challenges, we recommend that new versions focus on developing solutions to address these potential issues.
The study identified several limitations that could be considered for future enhancement. First, the search time window can only be specified annually. However, in real life, a more flexible time window is required. For example, if a user intends to identify cancer patients real-time using the system, he/she will need a narrower time window (e.g., month, week, or day). Second, the de-identified pipeline incorrectly de-identified some words or terms. For example, we noted that the word “mass” was constantly de-identified and resulted in an erroneous report. Third, when the TIES system codes the pathology report, a small percentage of reports erroneously combined all sections into a single “report section” rather than keeping the specific sections, such as “Clinical history,” “Final diagnosis,” “Gross description,” etc. In this instance, the TIES system searched the entire context of the report and was unable to limit to the truly “Final diagnosis” section. Such a misclassification could result in either false negative cases or false positive cases when a user searches with a specific section in a pathology report. As a result, three historical cases identified from the actual “Clinical history” section were not identified as historic cases and therefore misclassified as current potential cases. This type of misclassification could be avoided by using the section detection functionality provided by the TIES to properly configure sections with new section headers. Fourth, it seems the TIES engine is unable to accurately exclude the historic cases when the history term and breast cancer concept term are located in different sentences. For example, the system failed to recognize the historical nature when “History of” appeared in the first sentence, and “breast carcinoma” appeared in the following sentence. Finally, there were cases in which the negation should be applied to two or more conditions while the TIES system can only negate the condition close to the negation term. For example, the negation term “negative for” in the description “negative for dysplasia and malignancy” should be negated for the conditions of “dysplasia” and “malignancy”. However, the TIES system only highlighted “dysplasia” as negative diagnosis condition. Despite these limitations, our study demonstrated that the TIES system offers a robust and precise breast cancer case identification. In addition, the TIES system has the great potential to be easily applied to search for other cancer types, either single or multiple, with minimal development work.
| Conclusions|| |
We have successfully implemented the TIES system to import and process pathology reports on a daily basis. The validated results demonstrated that the TIES system can effectively identify the potential breast cancer cases within our care setting. All identified potential cases can be easily confirmed by reviewing the corresponding annotated reports through the front-end visualization interface. The TIES system is a useful NLP tool to identify cancer cases in a timely and efficient manner to support research studies and operational care management.
We would like to thank Philip Morales for the installation of the Linux server and the TIES system.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Committee on Quality of Health Care in America, Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st
Century. Washington, D.C.: Institute of Medicine; 2001.
Ludwick DA, Doucette J. Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries. Int J Med Inform 2009;78:22-31.
Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm 2015;68:232-7.
Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp 2000;270-4.
Rosenbloom ST, Denny JC, Xu H, Lorenzi N, Stead WW, Johnson KB. Data from clinical notes: A perspective on the tension between structure and flexible documentation. J Am Med Inform Assoc 2011;18:181-6.
Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, et al.
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
Crowley RS, Castine M, Mitchell K, Chavan G, McSherry T, Feldman M. CaTIES: A grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. J Am Med Inform Assoc 2010;17:253-64.
Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D. The Stanford CoreNLP Natural Language Processing Toolkit. Proceedings of 52nd
Annual Meeting of the Association for Computational Linguistics. System Demonstrations; 2014.
Zheng S, Lu JJ, Appin C, Brat D, Wang F. Support patient search on pathology reports with interactive online learning based data extraction. J Pathol Inform 2015;6:51.
] [Full text]
Doan S, Conway M, Phuong TM, Ohno-Machado L. Natural language processing in biomedicine: A unified system architecture overview. Methods Mol Biol 2014;1168:275-94.
Burger G, Abu-Hanna A, de Keizer N, Cornet R. Natural language processing in pathology: A scoping review. J Clin Pathol 2016; pii: jclinpath-2016-203872.
Schadow G, McDonald CJ. Extracting structured information from free text pathology reports. AMIA Annu Symp Proc 2003:584-8.
Mitchell KJ, Becich MJ, Berman JJ, Chapman WW, Gilbertson J, Gupta D, et al.
Implementation and evaluation of a negation tagger in a pipeline-based system for information extract from pathology reports. Stud Health Technol Inform 2004;107:663-7.
Schlangen D, Stede M, Bontas EP. Feeding OWL: Extracting and Representing the Content of Pathology Reports, in Proceeedings of the Workshop on NLP and XML (NLPXML-2004): RDF/RDFS and OWL in Language Technology. Association for Computational Linguistics; 2004. p. 43-50.
Carrell D, Miglioretti D, Smith-Bindman R. Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES). Proc AMIA Symp 2007;889.
Coden A, Savova G, Sominsky I, Tanenblatt M, Masanz J, Schuler K, et al.
Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model. J Biomed Inform 2009;42:937-49.
Yip V, Mete M, Topaloglu U, Kockara S. Concept discovery for pathology reports using an N-gram model. AMIA Jt Summits Transl Sci Proc 2010;2010:43-7.
Buckley JM, Coopey SB, Sharko J, Polubriaginof F, Drohan B, Belli AK, et al.
The feasibility of using natural language processing to extract clinical information from breast pathology reports. J Pathol Inform 2012;3:23.
] [Full text]
Ashish N, Dahm L, Boicey C. University of California, Irvine-Pathology Extraction Pipeline: The pathology extraction pipeline for information extraction from pathology reports. Health Informatics J 2014;20:288-305.
Jacobson RS, Becich MJ, Bollag RJ, Chavan G, Corrigan J, Dhir R, et al.
Afederated network for translational cancer research using clinical data and biospecimens. Cancer Res 2015;75:5194-201.
Tseytlin E, Mitchell K, Legowski E, Corrigan J, Chavan G, Jacobson RS, et al.
NOBLE - flexible concept recognition for large-scale biomedical natural language processing. BMC Bioinformatics 2016;17:32.
Evans MH, Rohm BW, Schultz FA, Kroth PJ. Using caTIES as a case-finding tool in tissue repositories: System challenges and lessons learned. National Cancer Institute Cancer Biomedical Informatics Grid (caBIG) Annual Meeting, Washington, DC; 2009.
Carrell DS, Halgrim S, Tran DT, Buist DS, Chubak J, Chapman WW, et al.
Using natural language processing to improve efficiency of manual chart abstraction in research: The case of breast cancer recurrence. Am J Epidemiol 2014;179:749-58.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer
| ||Danielle S. Bitterman, Timothy A. Miller, Raymond H. Mak, Guergana K. Savova |
| ||International Journal of Radiation Oncology*Biology*Physics. 2021; 110(3): 641 |
|[Pubmed] | [DOI]|
||Limitations of Transformers on Clinical Text Classification
| ||Shang Gao, Mohammed Alawad, M. Todd Young, John Gounley, Noah Schaefferkoetter, Hong Jun Yoon, Xiao-Cheng Wu, Eric B. Durbin, Jennifer Doherty, Antoinette Stroup, Linda Coyle, Georgia Tourassi |
| ||IEEE Journal of Biomedical and Health Informatics. 2021; 25(9): 3596 |
|[Pubmed] | [DOI]|
||Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text
| ||Zhou Yuan, Sean Finan, Jeremy Warner, Guergana Savova, Harry Hochheiser |
| ||JCO Clinical Cancer Informatics. 2020; (4): 412 |
|[Pubmed] | [DOI]|
||Artificial intelligence-driven structurization of diagnostic information in free-text pathology reports
| ||PericlesS Giannaris, Zainab Al-Taie, Mikhail Kovalenko, Nattapon Thanintorn, Olha Kholod, Yulia Innokenteva, Emily Coberly, Shellaine Frazier, Katsiarina Laziuk, Mihail Popescu, Chi-Ren Shyu, Dong Xu, RichardD Hammer, Dmitriy Shin |
| ||Journal of Pathology Informatics. 2020; 11(1): 4 |
|[Pubmed] | [DOI]|