Relevant bibliographies by topics / Clinical Natural Language Processing

Academic literature on the topic 'Clinical Natural Language Processing'

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Published: 25 May 2024

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Journal articles on the topic "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 (April 5, 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 (August 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 (July 1, 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 (March 1, 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 (March 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 will also discuss engineering issues which involve performance, robustness, and accessibility of the data from the end users' viewpoint.Also described are the three evaluations that have been performed on the system. The first evaluation was useful primarily for further refinement of the system. The two other evaluations involved an actual clinical application which consisted of retrieving reports that were associated with specified diseases. Automated queries were written by a medical expert based on the structured output forms generated as a result of text processing. The retrievals obtained by the automated system were compared to the retrievals obtained by independent medical experts who read the reports manually to determine whether they were associated with the specified diseases. MEDLEE was shown to perform comparably to the experts. The technique used to perform the last two evaluations was found to be a realistic evaluation technique for a natural language processor.
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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 (October 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, James J. Masanz, Pei Chen, Lee Becker, Wendy W. Chapman, Guergana K. Savova, Hongfang Liu, and Christopher G. Chute. "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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Legnar, Maximilian, Philipp Daumke, Jürgen Hesser, Stefan Porubsky, Zoran Popovic, Jan Niklas Bindzus, Joern-Helge Heinrich Siemoneit, and Cleo-Aron Weis. "Natural Language Processing in Diagnostic Texts from Nephropathology." Diagnostics 12, no. 7 (July 15, 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 based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.
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Mateussi, Nadayca, Michael P. Rogers, Emily A. Grimsley, Meagan Read, Rajavi Parikh, Ricardo Pietrobon, and Paul C. Kuo. "Clinical Applications of Machine Learning." Annals of Surgery Open 5, no. 2 (April 18, 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 reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework. Results: This review introduces interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning methodologies. Conclusions: Interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning are core machine learning methodologies that underlie many of the artificial intelligence methodologies that will drive the future of clinical medicine and surgery. End users must be well versed in the strengths and weaknesses of these tools as they are applied to patient care now and in the future.
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da Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, Juliana Machado-Rugolo, Luis Gustavo Modelli de Andrade, José Eduardo Corrente, and Liciana Vaz de Arruda Silveira. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records." Data 8, no. 1 (December 29, 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, creating a corpus with 1200 clinical texts. A natural language algorithm for text extraction and convolutional neural networks for pattern recognition were used to evaluate the model with goodness-of-fit indices. The results showed good accuracy, considering the complexity of the model, with an F-score of 63.9% and a precision of 72.7%. The patient condition class reached a precision of 90.3% and the medication class reached 87.5%. The proposed neural network will facilitate the detection of relationships between diseases and symptoms and prevalence and incidence, in addition to detecting the identification of clinical conditions, disease evolution, and the effects of prescribed medications.
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Dissertations / Theses on the topic "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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-65).
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 processing methods, including deep learning, rule-based, and classic machine learning, and applied to the identication of documentation reflecting advanced care planning measures, serious illnesses, and serious illness symptoms. I introduce two tools that can be used to analyze clinical notes from electronic health records: ClinicalRegex, a regular expression interface, and PyCCI, an a clinical text annotation tool. Additionally, I discuss a palliative care-focused research project in which I apply machine learning natural language processing methods to identifying clinical documentation in the palliative care and serious illness field. Advance care planning, which includes clarifying and documenting goals of care and preferences for future care, is essential for achieving end-of-life care that is consistent with the preferences of dying patients and their families. Physicians document their communication about these preferences as unstructured free text in clinical notes; as a result, routine assessment of this quality indicator is time consuming and costly. Integrating goals of care conversations and advance care planning into decision-making about palliative surgery have been shown to result in less invasive care near the time of death and improve clinical outcomes for both the patient and surviving family members. Natural language processing methods offer an efficient and scalable way to improve the visibility of documented serious illness conversations within electronic health record data, helping to better quality of care.
by Isabel Chien.
M. Eng.
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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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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 classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients’ family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations. The temporal dimension of extracted information from clinical records representing the trajectory of disease progression in patients was also studied in this project. Clinical data of patients who lived in Olmsted County (Rochester, MN) during 1966 to 2010 was analyzed in this work. The patient records were modeled by diagnosis matrices with clinical events as rows and their temporal information as columns. Deep learning algorithm was used to find common temporal patterns within these diagnosis matrices.

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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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 49-53).
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 resynchronization therapy (CRT) outcomes with better precision than the current physician guidelines for recommending the procedure. We combined NLP features from free-text physician notes with structured data to train a supervised classifier to predict CRT outcomes. While our results gave a slight improvement over the current baseline, we were not able to predict CRT outcome with both high precision and high recall. These results limit the clinical applicability of our model, and reinforce previous work, which also could not find accurate predictors of CRT response. The second task in this thesis was to extract breast cancer patient symptoms during chemotherapy from free-text physician notes. We manually annotated about 10,000 sentences, and trained a conditional random field (CRF) model to predict whether a word indicated a symptom (positive label), specifically indicated the absence of a symptom (negative label), or was neutral. Our final model achieved 0.66, 1.00, and 0.77 F1 scores for predicting positive, neutral, and negative labels respectively. While the F1 scores for positive and negative labels are not extremely high, with the current performance, our model could be applied, for example, to gather better statistics about what symptoms breast cancer patients experience during chemotherapy and at what time points during treatment they experience these symptoms.
by Alexander William Forsyth.
M. Eng.
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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.
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 list of potential answer candidates, i.e. MEDLINE® abstracts, that is reordered using new post-retrieval ranking strategies to ensure the most topically-relevant abstracts are displayed as high in the result set as possible. RetroRank achieves a significant improvement over the PubMed recency baseline and performs equal to or better than previous approaches to post-retrieval ranking relying on query frames and annotated data such as the approach by Demner-Fushman and Lin (2007). The performance of RetroRank shows that it is possible to successfully use natural language input and a fully automated approach to obtain answers to clinical drug comparison questions. This thesis also introduces two new evaluation corpora of clinical comparison questions with “gold standard” references that are freely available and are a valuable resource for future research in medical QA.
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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) classifier training algorithm in which a total of 22 classifiers including new classifier variants of AdaBoost and RandomForest were trained on seven different data views, and (vi) finally an ensemble learning step, in which ensembles of best learners were used to improve on the accuracy of individual learners. All of this was tested via standard 10-fold cross-validation on training data provided by the N-GRID challenge organizers, of which the three best ensembles were selected for submission to N-GRID's blind testing. The best of our submitted solutions garnered an overall final score of 0.863 according to the organizer's measure. All 3 of our submissions placed within the top 10 out of the 65 total submissions. The challenge constituted Track 2 of the 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID) Shared Task in Clinical Natural Language Processing.
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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 systems need to incorporate some knowledge of semantics. One approach exploits the distributional properties of language – more specifically, term co-occurrence information – to model the relative meaning of terms in high-dimensional vector space. Such methods have been used with success in a number of general language processing tasks; however, their application in the clinical domain has previously only been explored to a limited extent. By applying models of distributional semantics to clinical text, semantic spaces can be constructed in a completely unsupervised fashion. Semantic spaces of clinical text can then be utilized in a number of medically relevant applications. The application of distributional semantics in the clinical domain is here demonstrated in three use cases: (1) synonym extraction of medical terms, (2) assignment of diagnosis codes and (3) identification of adverse drug reactions. To apply distributional semantics effectively to a wide range of both general and, in particular, clinical language processing tasks, certain limitations or challenges need to be addressed, such as how to model the meaning of multiword terms and account for the function of negation: a simple means of incorporating paraphrasing and negation in a distributional semantic framework is here proposed and evaluated. The notion of ensembles of semantic spaces is also introduced; these are shown to outperform the use of a single semantic space on the synonym extraction task. This idea allows different models of distributional semantics, with different parameter configurations and induced from different corpora, to be combined. This is not least important in the clinical domain, as it allows potentially limited amounts of clinical data to be supplemented with data from other, more readily available sources. The importance of configuring the dimensionality of semantic spaces, particularly when – as is typically the case in the clinical domain – the vocabulary grows large, is also demonstrated.
De stora mängder kliniska data som genereras i patientjournalsystem är en underutnyttjad resurs med en enorm potential att förbättra hälso- och sjukvården. Då merparten av kliniska data är i form av ostrukturerad text, vilken är utmanande för datorer att analysera, finns det ett behov av sofistikerade metoder som kan behandla kliniskt språk. Metoder som inte kräver märkta exempel utan istället utnyttjar statistiska egenskaper i datamängden är särskilt värdefulla, med tanke på den begränsade tillgången till annoterade korpusar i den kliniska domänen. System för informationsextraktion och språkbehandling behöver innehålla viss kunskap om semantik. En metod går ut på att utnyttja de distributionella egenskaperna hos språk – mer specifikt, statistisk över hur termer samförekommer – för att modellera den relativa betydelsen av termer i ett högdimensionellt vektorrum. Metoden har använts med framgång i en rad uppgifter för behandling av allmänna språk; dess tillämpning i den kliniska domänen har dock endast utforskats i mindre utsträckning. Genom att tillämpa modeller för distributionell semantik på klinisk text kan semantiska rum konstrueras utan någon tillgång till märkta exempel. Semantiska rum av klinisk text kan sedan användas i en rad medicinskt relevanta tillämpningar. Tillämpningen av distributionell semantik i den kliniska domänen illustreras här i tre användningsområden: (1) synonymextraktion av medicinska termer, (2) tilldelning av diagnoskoder och (3) identifiering av läkemedelsbiverkningar. Det krävs dock att vissa begränsningar eller utmaningar adresseras för att möjliggöra en effektiv tillämpning av distributionell semantik på ett brett spektrum av uppgifter som behandlar språk – både allmänt och, i synnerhet, kliniskt – såsom hur man kan modellera betydelsen av flerordstermer och redogöra för funktionen av negation: ett enkelt sätt att modellera parafrasering och negation i ett distributionellt semantiskt ramverk presenteras och utvärderas. Idén om ensembler av semantisk rum introduceras också; dessa överträffer användningen av ett enda semantiskt rum för synonymextraktion. Den här metoden möjliggör en kombination av olika modeller för distributionell semantik, med olika parameterkonfigurationer samt inducerade från olika korpusar. Detta är inte minst viktigt i den kliniska domänen, då det gör det möjligt att komplettera potentiellt begränsade mängder kliniska data med data från andra, mer lättillgängliga källor. Arbetet påvisar också vikten av att konfigurera dimensionaliteten av semantiska rum, i synnerhet när vokabulären är omfattande, vilket är vanligt i den kliniska domänen.
High-Performance Data Mining for Drug Effect Detection (DADEL)
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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 interactivity. This research addresses these concerns for the GRiST by deploying data mining techniques. Clinical narratives and numerical data have both been analysed for this purpose. Clinical narratives have been processed by natural language processing (NLP)technology to extract knowledge from them. SNOMED-CT was used as a reference ontology and the performance of the different extraction algorithms have been compared. A new Ensemble Concept Mining (ECM) method has been proposed, which may eliminate the need for domain specific phrase annotation requirements. Word embedding has been used to filter phrases semantically and to build a semantic representation of each of the GRiST ontology nodes. The Chi-square and FP-growth methods have been used to find relationships between GRiST ontology nodes. Interesting patterns have been found that could be used to provide real-time feedback to clinicians. Information gain has been used efficaciously to explain the differences between the clinicians and the consensus risk. A new risk management strategy has been explored by analysing repeat assessments. A few novel methods have been proposed to perform automatic background analysis of the patient data and improve the interactivity and reliability of GRiST and similar systems.
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Books on the topic "Clinical Natural Language Processing"

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Guo, Shuli, Lina Han, and Wentao Yang. Clinical Chinese Named Entity Recognition in Natural Language Processing. Singapore: 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. [New York, N.Y.?]: [publisher not identified], 2022.

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

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Natural language processing. Oxford, OX: Blackwell Scientific Publications, 1988.

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

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

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Søgaard, Anders. Explainable Natural Language Processing. Cham: 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. Cham: 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. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7.

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Book chapters on the topic "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, 159–88. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-8.

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Hasan, Sadid A., and Oladimeji Farri. "Clinical Natural Language Processing with Deep Learning." In Data Science for Healthcare, 147–71. Cham: 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, 1–17. Boca Raton: 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, 58–73. New York: 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, 359–65. Cham: 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, 82–93. Berlin, Heidelberg: 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, 229–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47994-7_14.

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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, 195–207. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23281-8_16.

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Kaiser, Katharina, and Silvia Miksch. "Supporting the Abstraction of Clinical Practice Guidelines Using Information Extraction." In Natural Language Processing and Information Systems, 304–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13881-2_32.

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Soto, Xabier, Olatz Perez-de-Viñaspre, Maite Oronoz, and Gorka Labaka. "Development of a Machine Translation System for Promoting the Use of a Low Resource Language in the Clinical Domain: The Case of Basque." In Natural Language Processing in Healthcare, 139–58. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-7.

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Conference papers on the topic "Clinical Natural Language Processing"

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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. Stroudsburg, PA, USA: 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. New York, NY, USA: 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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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. Stroudsburg, PA, USA: 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, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. "Publicly Available Clinical." In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-1909.

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Huang, Kexin, Abhishek Singh, Sitong Chen, Edward Moseley, Chih-Ying Deng, Naomi George, and Charolotta Lindvall. "Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation." In Proceedings of the 3rd Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.clinicalnlp-1.11.

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Shor, Joel, Ruyue Agnes Bi, Subhashini Venugopalan, Steven Ibara, Roman Goldenberg, and Ehud Rivlin. "Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings." In Proceedings of the 5th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.1.

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Wang, Yanshan, Ahmad Tafti, Sunghwan Sohn, and Rui Zhang. "Applications of Natural Language Processing in Clinical Research and Practice." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-5006.

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Reports on the topic "Clinical Natural Language Processing"

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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.

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Abstract:
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 interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.
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Steedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada290396.

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Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, June 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. Fort Belvoir, VA: Defense Technical Information Center, January 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), February 2005. http://dx.doi.org/10.2172/15015192.

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Anderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, November 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. Fort Belvoir, VA: Defense Technical Information Center, January 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. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada263301.

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

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Wallace, Byron C. Sociolinguistically Informed Natural Language Processing: Automating Irony Detection. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada623456.

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