Thematische Bibliographien / Clinical Natural Language Processing

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Clinical Natural Language Processing“

Autor: Grafiati
Veröffentlicht am 25. Mai 2024
Zuletzt aktualisiert am 19. Juli 2025

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Zeitschriftenartikel zum Thema "Clinical Natural Language Processing"

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K Gautam, Leena. "Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1541–44. http://dx.doi.org/10.21275/sr24422134801.

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Yandell, Mark D., and William H. Majoros. "Genomics and natural language processing." Nature Reviews Genetics 3, no. 8 (2002): 601–10. http://dx.doi.org/10.1038/nrg861.

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Ware, H., C. J. Mullett, and V. Jagannathan. "Natural Language Processing Framework to Assess Clinical Conditions." Journal of the American Medical Informatics Association 16, no. 4 (2009): 585–89. http://dx.doi.org/10.1197/jamia.m3091.

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Sager, N., M. Lyman, C. Bucknall, N. Nhan, and L. J. Tick. "Natural Language Processing and the Representation of Clinical Data." Journal of the American Medical Informatics Association 1, no. 2 (1994): 142–60. http://dx.doi.org/10.1136/jamia.1994.95236145.

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Friedman, C., G. Hripcsak, W. DuMouchel, S. B. Johnson, and P. D. Clayton. "Natural language processing in an operational clinical information system." Natural Language Engineering 1, no. 1 (1995): 83–108. http://dx.doi.org/10.1017/s1351324900000061.

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AbstractThis paper describes a natural language text extraction system, called MEDLEE, that has been applied to the medical domain. The system extracts, structures, and encodes clinical information from textual patient reports. It was integrated with the Clinical Information System (CIS), which was developed at Columbia-Presbyterian Medical Center (CPMC) to help improve patient care. MEDLEE is currently used on a daily basis to routinely process radiological reports of patients at CPMC.In order to describe how the natural language system was made compatible with the existing CIS, this paper wi
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Denny, Joshua C., Lisa Bastarache, Elizabeth Ann Sastre, and Anderson Spickard. "Tracking medical students’ clinical experiences using natural language processing." Journal of Biomedical Informatics 42, no. 5 (2009): 781–89. http://dx.doi.org/10.1016/j.jbi.2009.02.004.

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Wu, Stephen T., Vinod C. Kaggal, Dmitriy Dligach, et al. "A common type system for clinical natural language processing." Journal of Biomedical Semantics 4, no. 1 (2013): 1. http://dx.doi.org/10.1186/2041-1480-4-1.

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Mateussi, Nadayca, Michael P. Rogers, Emily A. Grimsley, et al. "Clinical Applications of Machine Learning." Annals of Surgery Open 5, no. 2 (2024): e423. http://dx.doi.org/10.1097/as9.0000000000000423.

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Objective: This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background: As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies. Methods: This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinfo
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Legnar, Maximilian, Philipp Daumke, Jürgen Hesser, et al. "Natural Language Processing in Diagnostic Texts from Nephropathology." Diagnostics 12, no. 7 (2022): 1726. http://dx.doi.org/10.3390/diagnostics12071726.

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Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group base
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da Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, et al. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records." Data 8, no. 1 (2022): 11. http://dx.doi.org/10.3390/data8010011.

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Studies that use medical records are often impeded due to the information presented in narrative fields. However, recent studies have used artificial intelligence to extract and process secondary health data from electronic medical records. The aim of this study was to develop a neural network that uses data from unstructured medical records to capture information regarding symptoms, diagnoses, medications, conditions, exams, and treatment. Data from 30,000 medical records of patients hospitalized in the Clinical Hospital of the Botucatu Medical School (HCFMB), São Paulo, Brazil, were obtained
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Dissertationen zum Thema "Clinical Natural Language Processing"

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Chien, Isabel. "Natural language processing for precision clinical diagnostics and treatment." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119754.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-65).<br>In this thesis, I focus upon application of natural language processing to clinical diagnostics and treatment within the palliative care and serious illness field. I explore a variety of natural language processin
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Mehrabi, Saeed. "Advanced natural language processing and temporal mining for clinical discovery." Thesis, Indiana University - Purdue University Indianapolis, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10032405.

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<p> There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classi
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Forsyth, Alexander William. "Improving clinical decision making with natural language processing and machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112847.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 49-53).<br>This thesis focused on two tasks of applying natural language processing (NLP) and machine learning to electronic health records (EHRs) to improve clinical decision making. The first task was to predict cardiac re
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Regulapati, Sushmitha. "Natural language processing framework to assist in the evaluation of adherence to clinical guidelines." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5340.

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Thesis (M.S.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains vii, 36 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 33-36).
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Leonhard, Annette Christa. "Automated question answering for clinical comparison questions." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6266.

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This thesis describes the development and evaluation of new automated Question Answering (QA) methods tailored to clinical comparison questions that give clinicians a rank-ordered list of MEDLINE® abstracts targeted to natural language clinical drug comparison questions (e.g. ”Have any studies directly compared the effects of Pioglitazone and Rosiglitazone on the liver?”). Three corpora were created to develop and evaluate a new QA system for clinical comparison questions called RetroRank. RetroRank takes the clinician’s plain text question as input, processes it and outputs a rank-ordered lis
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Eglowski, Skylar. "CREATE: Clinical Record Analysis Technology Ensemble." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1771.

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In this thesis, we describe an approach that won a psychiatric symptom severity prediction challenge. The challenge was to correctly predict the severity of psychiatric symptoms on a 4-point scale. Our winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new features, followed by a (iv) feature selection step to eliminate irrelevant features, followed by a (v)
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Wang, Yefeng. "Information extraction from clinical notes." Thesis, The University of Sydney, 2010. https://hdl.handle.net/2123/28844.

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Information Extraction (IE) is an important task for Natural Language Processing (NLP). Effective IE methods, aimed at constructing structured information for unstructured natural language text, can reduce a large amount of human effort in processing the digital information available today. Successful application of IE to the clinical domain can advance clinical research and provide underlying techniques to support better health information systems. This thesis investigates the problems of IE from clinical notes.
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Henriksson, Aron. "Semantic Spaces of Clinical Text : Leveraging Distributional Semantics for Natural Language Processing of Electronic Health Records." Licentiate thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-94344.

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The large amounts of clinical data generated by electronic health record systems are an underutilized resource, which, if tapped, has enormous potential to improve health care. Since the majority of this data is in the form of unstructured text, which is challenging to analyze computationally, there is a need for sophisticated clinical language processing methods. Unsupervised methods that exploit statistical properties of the data are particularly valuable due to the limited availability of annotated corpora in the clinical domain. Information extraction and natural language processing system
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Khizra, Shufa. "Using Natural Language Processing and Machine Learning for Analyzing Clinical Notes in Sickle Cell Disease Patients." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright154759374321405.

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Islam, Mohammed Ashrafull. "Enhancing the interactivity of a clinical decision support system by using knowledge engineering and natural language processing." Thesis, Aston University, 2018. http://publications.aston.ac.uk/37540/.

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Mental illness is a serious health problem and it affects many people. Increasingly,Clinical Decision Support Systems (CDSS) are being used for diagnosis and it is important to improve the reliability and performance of these systems. Missing a potential clue or a wrong diagnosis can have a detrimental effect on the patient's quality of life and could lead to a fatal outcome. The context of this research is the Galatean Risk and Safety Tool (GRiST), a mental-health-risk assessment system. Previous research has shown that success of a CDSS depends on its ease of use, reliability and interactivi
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Bücher zum Thema "Clinical Natural Language Processing"

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Guo, Shuli, Lina Han, and Wentao Yang. Clinical Chinese Named Entity Recognition in Natural Language Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2665-7.

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Idnay, Betina Ross Saldua. Improving Eligibility Prescreening for Alzheimer’s Disease and Related Dementias Clinical Trials with Natural Language Processing. [publisher not identified], 2022.

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Filgueiras, M., L. Damas, N. Moreira, and A. P. Tomás, eds. Natural Language Processing. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.

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Lee, Raymond S. T. Natural Language Processing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-1999-4.

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Lee, Raymond. Natural Language Processing. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3208-4.

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Kulkarni, Akshay, and Adarsha Shivananda. Natural Language Processing Recipes. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.

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Kulkarni, Akshay, Adarsha Shivananda, and Anoosh Kulkarni. Natural Language Processing Projects. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.

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Søgaard, Anders. Explainable Natural Language Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02180-0.

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Tapsai, Chalermpol, Herwig Unger, and Phayung Meesad. Thai Natural Language Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56235-9.

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Oflazer, Kemal, and Murat Saraçlar, eds. Turkish Natural Language Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7.

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Buchteile zum Thema "Clinical Natural Language Processing"

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Khose, Bhasha, and Yashodhara V. Haribhakta. "Clinical NLP for Drug Safety." In Natural Language Processing in Healthcare. CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-8.

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Garg, Geetanjali, and Shobha Bhatt. "Generative Large Language Models in Clinical, Legal and Financial Domains." In Transformative Natural Language Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88988-2_9.

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Hasan, Sadid A., and Oladimeji Farri. "Clinical Natural Language Processing with Deep Learning." In Data Science for Healthcare. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05249-2_5.

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Baruah, Rupjyoti, and Anil Kumar Singh. "A Clinical Practice by Machine Translation on Low Resource Languages." In Natural Language Processing in Healthcare. CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-1.

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Harik, Polina, Janet Mee, Christopher Runyon, and Brian E. Clauser. "Assessment of Clinical Skills." In Advancing Natural Language Processing in Educational Assessment. Routledge, 2023. http://dx.doi.org/10.4324/9781003278658-5.

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Dehghan, Azad, Tom Liptrot, Daniel Tibble, Matthew Barker-Hewitt, and Goran Nenadic. "Identification of Occupation Mentions in Clinical Narratives." In Natural Language Processing and Information Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41754-7_35.

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Lonsdale, Deryle, Clint Tustison, Craig Parker, and David W. Embley. "Formulating Queries for Assessing Clinical Trial Eligibility." In Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11765448_8.

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Celi, Leo Anthony, Christina Chen, Daniel Gruhl, Chaitanya Shivade, and Joy Tzung-Yu Wu. "Introduction to Clinical Natural Language Processing with Python." In Leveraging Data Science for Global Health. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47994-7_14.

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Demner Fushman, Dina, and Hua Xu. "Introduction to Natural Language Processing of Clinical Text." In Cognitive Informatics in Biomedicine and Healthcare. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55865-8_1.

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Gangavarapu, Tushaar, Aditya Jayasimha, Gokul S. Krishnan, and Sowmya Kamath S. "TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes." In Natural Language Processing and Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23281-8_16.

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Konferenzberichte zum Thema "Clinical Natural Language Processing"

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Ji, Yuelyu, Zeshui Yu, and Yanshan Wang. "Assertion Detection in Clinical Natural Language Processing Using Large Language Models." In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). IEEE, 2024. http://dx.doi.org/10.1109/ichi61247.2024.00039.

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Chen, Aokun, Daniel Paredes, Zehao Yu, et al. "Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing." In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). IEEE, 2024. http://dx.doi.org/10.1109/ichi61247.2024.00046.

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Arias, Juan F. "Natural Language Processing for Clinical Quality Measures." In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2023. http://dx.doi.org/10.1109/cbms58004.2023.00255.

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Rojas, Matías, Jocelyn Dunstan, and Fabián Villena. "Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing." In Proceedings of the 4th Clinical Natural Language Processing Workshop. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.clinicalnlp-1.9.

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Trivedi, Gaurav. "Towards Interactive Natural Language Processing in Clinical Care." In 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2018. http://dx.doi.org/10.1109/ichi.2018.00096.

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Trivedi, Gaurav. "Clinical Text Analysis Using Interactive Natural Language Processing." In IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces. ACM, 2015. http://dx.doi.org/10.1145/2732158.2732162.

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Boytcheva, Svetla, Galia Angelova, and Zhivko Angelov. "Risk Factors Extraction from Clinical Texts based on Linked Open Data." In Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_019.

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Yang, Zhichao, Avijit Mitra, Sunjae Kwon, and Hong Yu. "ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes." In Proceedings of the 6th Clinical Natural Language Processing Workshop. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.clinicalnlp-1.5.

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Liu, Ming, Richard Beare, Taya Collyer, Nadine Andrew, and Velandai Srikanth. "Leveraging Natural Language Processing and Clinical Notes for Dementia Detection." In Proceedings of the 5th Clinical Natural Language Processing Workshop. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.20.

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Alsentzer, Emily, John Murphy, William Boag, et al. "Publicly Available Clinical." In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-1909.

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Berichte der Organisationen zum Thema "Clinical Natural Language Processing"

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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, et al. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.

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Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries intereste
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Steedman, Mark. Natural Language Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290396.

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Bergeaud, Antonin, Adam Jaffe, and Dimitris Papanikolaou. Natural Language Processing and Innovation Research. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33821.

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Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603814.

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Wilks, Yorick, Michael Coombs, Roger T. Hartley, and Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada245893.

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Firpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/15015192.

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Anderson, Thomas. State of the Art of Natural Language Processing. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada188112.

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Hobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson, and David Magerman. Robust Processing of Real-World Natural-Language Texts. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada258837.

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Neal, Jeannette G., Elissa L. Feit, Douglas J. Funke, and Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada263301.

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Lehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273538.

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