Journal articles: '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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Friedman, Carol, Lyudmila Shagina, Yves Lussier, and George Hripcsak. "Automated Encoding of Clinical Documents Based on Natural Language Processing." Journal of the American Medical Informatics Association 11, no. 5 (September 2004): 392–402. http://dx.doi.org/10.1197/jamia.m1552.

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Demner-Fushman, Dina, Wendy W. Chapman, and Clement J. McDonald. "What can natural language processing do for clinical decision support?" Journal of Biomedical Informatics 42, no. 5 (October 2009): 760–72. http://dx.doi.org/10.1016/j.jbi.2009.08.007.

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Leaman, Robert, Ritu Khare, and Zhiyong Lu. "Challenges in clinical natural language processing for automated disorder normalization." Journal of Biomedical Informatics 57 (October 2015): 28–37. http://dx.doi.org/10.1016/j.jbi.2015.07.010.

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Kalyan, Katikapalli Subramanyam, and S. Sangeetha. "SECNLP: A survey of embeddings in clinical natural language processing." Journal of Biomedical Informatics 101 (January 2020): 103323. http://dx.doi.org/10.1016/j.jbi.2019.103323.

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Cai, Tianrun, Andreas A. Giannopoulos, Sheng Yu, Tatiana Kelil, Beth Ripley, Kanako K. Kumamaru, Frank J. Rybicki, and Dimitrios Mitsouras. "Natural Language Processing Technologies in Radiology Research and Clinical Applications." RadioGraphics 36, no. 1 (January 2016): 176–91. http://dx.doi.org/10.1148/rg.2016150080.

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Wu, Stephen, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, et al. "Deep learning in clinical natural language processing: a methodical review." Journal of the American Medical Informatics Association 27, no. 3 (December 3, 2019): 457–70. http://dx.doi.org/10.1093/jamia/ocz200.

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Abstract Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. Materials and Methods We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. Results DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a “long tail” of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. Discussion Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). Conclusion Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.

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Bobba, Pratheek S., Anne Sailer, James A. Pruneski, Spencer Beck, Ali Mozayan, Sara Mozayan, Jennifer Arango, Arman Cohan, and Sophie Chheang. "Natural language processing in radiology: Clinical applications and future directions." Clinical Imaging 97 (May 2023): 55–61. http://dx.doi.org/10.1016/j.clinimag.2023.02.014.

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Luo, Jack W., and Jaron J. R. Chong. "Review of Natural Language Processing in Radiology." Neuroimaging Clinics of North America 30, no. 4 (November 2020): 447–58. http://dx.doi.org/10.1016/j.nic.2020.08.001.

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Diab, Kareem Mahmoud, Jamie Deng, Yusen Wu, Yelena Yesha, Fernando Collado-Mesa, and Phuong Nguyen. "Natural Language Processing for Breast Imaging: A Systematic Review." Diagnostics 13, no. 8 (April 14, 2023): 1420. http://dx.doi.org/10.3390/diagnostics13081420.

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Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.

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Karhade, Aditya V., Michiel E. R. Bongers, Olivier Q. Groot, Erick R. Kazarian, Thomas D. Cha, Harold A. Fogel, Stuart H. Hershman, et al. "Natural language processing for automated detection of incidental durotomy." Spine Journal 20, no. 5 (May 2020): 695–700. http://dx.doi.org/10.1016/j.spinee.2019.12.006.

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Ozonoff, Al, Carly E. Milliren, Kerri Fournier, Jennifer Welcher, Assaf Landschaft, Mihail Samnaliev, Mehmet Saluvan, Mark Waltzman, and Amir A. Kimia. "Electronic surveillance of patient safety events using natural language processing." Health Informatics Journal 28, no. 4 (October 2022): 146045822211324. http://dx.doi.org/10.1177/14604582221132429.

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Objective We describe our approach to surveillance of reportable safety events captured in hospital data including free-text clinical notes. We hypothesize that a) some patient safety events are documented only in the clinical notes and not in any other accessible source; and b) large-scale abstraction of event data from clinical notes is feasible. Materials and Methods We use regular expressions to generate a training data set for a machine learning model and apply this model to the full set of clinical notes and conduct further review to identify safety events of interest. We demonstrate this approach on peripheral intravenous (PIV) infiltrations and extravasations (PIVIEs). Results During Phase 1, we collected 21,362 clinical notes, of which 2342 were reviewed. We identified 125 PIV events, of which 44 cases (35%) were not captured by other patient safety systems. During Phase 2, we collected 60,735 clinical notes and identified 440 infiltrate events. Our classifier demonstrated accuracy above 90%. Conclusion Our method to identify safety events from the free text of clinical documentation offers a feasible and scalable approach to enhance existing patient safety systems. Expert reviewers, using a machine learning model, can conduct routine surveillance of patient safety events.

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ALLA, Naga Lalitha Valli, Aipeng CHEN, Sean BATONGBACAL, Chandini NEKKANTTI, Hong-Jie Dai, and Jitendra JONNAGADDALA. "Cohort selection for construction of a clinical natural language processing corpus." Computer Methods and Programs in Biomedicine Update 1 (2021): 100024. http://dx.doi.org/10.1016/j.cmpbup.2021.100024.

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Sheikhalishahi, Seyedmostafa, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli, Fabio Rinaldi, and Venet Osmani. "Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review." JMIR Medical Informatics 7, no. 2 (April 27, 2019): e12239. http://dx.doi.org/10.2196/12239.

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Wu, Stephen, Timothy Miller, James Masanz, Matt Coarr, Scott Halgrim, David Carrell, and Cheryl Clark. "Negation’s Not Solved: Generalizability Versus Optimizability in Clinical Natural Language Processing." PLoS ONE 9, no. 11 (November 13, 2014): e112774. http://dx.doi.org/10.1371/journal.pone.0112774.

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CALVO, RAFAEL A., DAVID N. MILNE, M. SAZZAD HUSSAIN, and HELEN CHRISTENSEN. "Natural language processing in mental health applications using non-clinical texts." Natural Language Engineering 23, no. 5 (January 30, 2017): 649–85. http://dx.doi.org/10.1017/s1351324916000383.

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AbstractNatural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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Névéol, A., and P. Zweigenbaum. "Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare." Yearbook of Medical Informatics 24, no. 01 (August 2015): 194–98. http://dx.doi.org/10.15265/iy-2015-035.

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Summary Objective: To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP).Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that the field is tackling text analysis methods of increasing depth. The full review process highlighted five papers addressing foundational methods in clinical NLP using clinically relevant texts from online forums or encyclopedias, clinical texts from Electronic Health Records, and included studies specifically aiming at a practical clinical outcome. The increased access to clinical data that was made possible with the recent progress of de-identification paved the way for the scientific community to address complex NLP problems such as word sense disambiguation, negation, temporal analysis and specific information nugget extraction. These advances in turn allowed for efficient application of NLP to clinical problems such as cancer patient triage. Another line of research investigates online clinically relevant texts and brings interesting insight on communication strategies to convey health-related information. Conclusions: The field of clinical NLP is thriving through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques for concrete healthcare purposes. Clinical NLP is becoming mature for practical applications with a significant clinical impact.

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Trivedi, Gaurav, Phuong Pham, Wendy W. Chapman, Rebecca Hwa, Janyce Wiebe, and Harry Hochheiser. "NLPReViz: an interactive tool for natural language processing on clinical text." Journal of the American Medical Informatics Association 25, no. 1 (July 22, 2017): 81–87. http://dx.doi.org/10.1093/jamia/ocx070.

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Abstract The gap between domain experts and natural language processing expertise is a barrier to extracting understanding from clinical text. We describe a prototype tool for interactive review and revision of natural language processing models of binary concepts extracted from clinical notes. We evaluated our prototype in a user study involving 9 physicians, who used our tool to build and revise models for 2 colonoscopy quality variables. We report changes in performance relative to the quantity of feedback. Using initial training sets as small as 10 documents, expert review led to final F1scores for the “appendiceal-orifice” variable between 0.78 and 0.91 (with improvements ranging from 13.26% to 29.90%). F1for “biopsy” ranged between 0.88 and 0.94 (−1.52% to 11.74% improvements). The average System Usability Scale score was 70.56. Subjective feedback also suggests possible design improvements.

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Pethani, Farhana, and Adam G. Dunn. "Natural language processing for clinical notes in dentistry: A systematic review." Journal of Biomedical Informatics 138 (February 2023): 104282. http://dx.doi.org/10.1016/j.jbi.2023.104282.

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Li, Chengtai, Yiming Zhang, Ying Weng, Boding Wang, and Zhenzhu Li. "Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology." Diagnostics 13, no. 2 (January 12, 2023): 286. http://dx.doi.org/10.3390/diagnostics13020286.

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In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.

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Miskov-Zivanov, Natasa, and Stefan Andjelkovic. "Natural language processing to aggregate knowledge of biological networks." Brain Stimulation 14, no. 6 (November 2021): 1713. http://dx.doi.org/10.1016/j.brs.2021.10.411.

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Hripcsak, G., and C. Friedman. "Evaluating Natural Language Processors in the Clinical Domain." Methods of Information in Medicine 37, no. 04/05 (October 1998): 334–44. http://dx.doi.org/10.1055/s-0038-1634566.

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AbstractEvaluating natural language processing (NLP) systems in the clinical domain is a difficult task which is important for advancement of the field. A number of NLP systems have been reported that extract information from free-text clinical reports, but not many of the systems have been evaluated. Those that were evaluated noted good performance measures but the results were often weakened by ineffective evaluation methods. In this paper we describe a set of criteria aimed at improving the quality of NLP evaluation studies. We present an overview of NLP evaluations in the clinical domain and also discuss the Message Understanding Conferences (MUC) [1-41. Although these conferences constitute a series of NLP evaluation studies performed outside of the clinical domain, some of the results are relevant within medicine. In addition, we discuss a number of factors which contribute to the complexity that is inherent in the task of evaluating natural language systems.

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Shaitarova, Anastassia, Jamil Zaghir, Alberto Lavelli, Michael Krauthammer, and Fabio Rinaldi. "Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey." Yearbook of Medical Informatics 32, no. 01 (August 2023): 230–43. http://dx.doi.org/10.1055/s-0043-1768726.

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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

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Joffe, Erel, Emily J. Pettigrew, Jorge R. Herskovic, Charles F. Bearden, and Elmer V. Bernstam. "Expert guided natural language processing using one-class classification." Journal of the American Medical Informatics Association 22, no. 5 (June 10, 2015): 962–66. http://dx.doi.org/10.1093/jamia/ocv010.

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Abstract Introduction Automatically identifying specific phenotypes in free-text clinical notes is critically important for the reuse of clinical data. In this study, the authors combine expert-guided feature (text) selection with one-class classification for text processing. Objectives To compare the performance of one-class classification to traditional binary classification; to evaluate the utility of feature selection based on expert-selected salient text (snippets); and to determine the robustness of these models with respects to irrelevant surrounding text. Methods The authors trained one-class support vector machines (1C-SVMs) and two-class SVMs (2C-SVMs) to identify notes discussing breast cancer. Manually annotated visit summary notes (88 positive and 88 negative for breast cancer) were used to compare the performance of models trained on whole notes labeled as positive or negative to models trained on expert-selected text sections (snippets) relevant to breast cancer status. Model performance was evaluated using a 70:30 split for 20 iterations and on a realistic dataset of 10 000 records with a breast cancer prevalence of 1.4%. Results When tested on a balanced experimental dataset, 1C-SVMs trained on snippets had comparable results to 2C-SVMs trained on whole notes (F = 0.92 for both approaches). When evaluated on a realistic imbalanced dataset, 1C-SVMs had a considerably superior performance (F = 0.61 vs. F = 0.17 for the best performing model) attributable mainly to improved precision (p = .88 vs. p = .09 for the best performing model). Conclusions 1C-SVMs trained on expert-selected relevant text sections perform better than 2C-SVMs classifiers trained on either snippets or whole notes when applied to realistically imbalanced data with low prevalence of the positive class.

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Kanaparthi, Vijaya. "Examining Natural Language Processing Techniques in the Education and Healthcare Fields." International Journal of Engineering and Advanced Technology 12, no. 2 (December 30, 2022): 8–18. http://dx.doi.org/10.35940/ijeat.b3861.1212222.

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Natural language processing is a branch of artificial intelligence currently being used to classify unstructured data. While natural language processing is found throughout several fields, these algorithms are currently being excelled in the education and healthcare fields. The healthcare industry has found various uses of natural language processing models. These algorithms are capable of analyzing large amounts of unstructured data from clinical notes, making it easier for healthcare professionals to identify at-risk patients and analyze consumer healthcare perception. In the education field, researchers are utilizing natural language processing models to enhance student academic success, reading comprehension, and to evaluate the fairness of student evaluations. Both fields have been able to find use of natural language model processing models. Some business leaders, however, are fearful of natural language processing. This review seeks to explore the various uses of natural language processing in the healthcare and education fields to determine the benefit and disadvantages these models have on both fields.

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Zweigenbaum, P., and A. Névéol. "Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest." Yearbook of Medical Informatics 25, no. 01 (August 2016): 234–39. http://dx.doi.org/10.15265/iy-2016-049.

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Summary Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects. Conclusions: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.

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Amer, Eslam. "Enhancing Disability Determination Decision Process Through Natural Language Processing." International Journal of Applied Research on Public Health Management 4, no. 2 (July 2019): 15–28. http://dx.doi.org/10.4018/ijarphm.2019070102.

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In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed that the proposed approach accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). Also, results showed an excellent deduction in assessments to approve or reject an applicant case to obtain a disability benefit.

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Koonce, Taneya Y., Dario A. Giuse, Annette M. Williams, Mallory N. Blasingame, Poppy A. Krump, Jing Su, and Nunzia B. Giuse. "Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition." JMIR Medical Informatics 12 (January 30, 2024): e53516. http://dx.doi.org/10.2196/53516.

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Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC’s electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.

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Dorda, W., B. Haidl, and P. Sachs. "Processing Medical Natural Language Data by the System WAREL." Methods of Information in Medicine 27, no. 02 (April 1988): 67–72. http://dx.doi.org/10.1055/s-0038-1635521.

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SummaryMany clinical data are in natural language form (diagnoses, therapies, etc.). There is great interest in making these data retrievable to form samples of patients for scientific investigations (statistical analyses, courses of diseases, etc.). To perform this task, “medical natural language data” have to be prepared and stored in a retrieval-oriented database. In this paper, the advantages of processing textual data are shown in contrast to coding. Accordingly, in our system WAREL medical thesauri (like ICD 9 or SNOMED) are not used for codification; they are taken as a knowledge base during the retrieval and for testing the quality of the data during documentation. The fundamental methods (computerized textual analysis and different algorithms for comparing texts) are explained in detail, and their realization within the system WAREL is illustrated (WAREL stands for Wiener Allgemeines Relationenschema).

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Ganguly, Indrila, Graham Buhrman, Ed Kline, Seong K. Mun, and Srijan Sengupta. "Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing." Diagnostics 13, no. 7 (March 23, 2023): 1215. http://dx.doi.org/10.3390/diagnostics13071215.

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A report published in 2000 from the Institute of Medicine revealed that medical errors were a leading cause of patient deaths, and urged the development of error detection and reporting systems. The field of radiation oncology is particularly vulnerable to these errors due to its highly complex process workflow, the large number of interactions among various systems, devices, and medical personnel, as well as the extensive preparation and treatment delivery steps. Natural language processing (NLP)-aided statistical algorithms have the potential to significantly improve the discovery and reporting of these medical errors by relieving human reporters of the burden of event type categorization and creating an automated, streamlined system for error incidents. In this paper, we demonstrate text-classification models developed with clinical data from a full service radiation oncology center (test center) that can predict the broad level and first level category of an error given a free-text description of the error. All but one of the resulting models had an excellent performance as quantified by several metrics. The results also suggest that more development and more extensive training data would further improve future results.

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Bitterman, Danielle S., Timothy A. Miller, Raymond H. Mak, and Guergana K. Savova. "Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer." International Journal of Radiation Oncology*Biology*Physics 110, no. 3 (July 2021): 641–55. http://dx.doi.org/10.1016/j.ijrobp.2021.01.044.

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Kormilitzin, Andrey, Nemanja Vaci, Qiang Liu, and Alejo Nevado-Holgado. "Med7: A transferable clinical natural language processing model for electronic health records." Artificial Intelligence in Medicine 118 (August 2021): 102086. http://dx.doi.org/10.1016/j.artmed.2021.102086.

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Feller, Daniel J., Jason Zucker, Michael T. Yin, Peter Gordon, and Noémie Elhadad. "Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment." JAIDS Journal of Acquired Immune Deficiency Syndromes 77, no. 2 (February 2018): 160–66. http://dx.doi.org/10.1097/qai.0000000000001580.

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Hripcsak, George. "Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing." Annals of Internal Medicine 122, no. 9 (May 1, 1995): 681. http://dx.doi.org/10.7326/0003-4819-122-9-199505010-00007.

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Afzal, Naveed, Vishnu Priya Mallipeddi, Sunghwan Sohn, Hongfang Liu, Rajeev Chaudhry, Christopher G. Scott, Iftikhar J. Kullo, and Adelaide M. Arruda-Olson. "Natural language processing of clinical notes for identification of critical limb ischemia." International Journal of Medical Informatics 111 (March 2018): 83–89. http://dx.doi.org/10.1016/j.ijmedinf.2017.12.024.

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Mowery, D., B. R. South, M. Kvist, H. Dalianis, and S. Velupillai. "Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis." Yearbook of Medical Informatics 24, no. 01 (August 2015): 183–93. http://dx.doi.org/10.15265/iy-2015-009.

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Summary Objectives: We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis. Methods: We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers. Results: Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications. Conclusions: There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.

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Taboada, M., M. Meizoso, D. Martínez, D. Riaño, and A. Alonso. "Combining open-source natural language processing tools to parse clinical practice guidelines." Expert Systems 30, no. 1 (January 24, 2011): 3–11. http://dx.doi.org/10.1111/j.1468-0394.2010.00575.x.

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Soysal, Ergin, Jingqi Wang, Min Jiang, Yonghui Wu, Serguei Pakhomov, Hongfang Liu, and Hua Xu. "CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines." Journal of the American Medical Informatics Association 25, no. 3 (November 24, 2017): 331–36. http://dx.doi.org/10.1093/jamia/ocx132.

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Abstract Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding. We also demonstrate the efficiency of the CLAMP graphic user interface in building customized, high-performance NLP pipelines with 2 use cases, extracting smoking status and lab test values. CLAMP is publicly available for research use, and we believe it is a unique asset for the clinical NLP community.

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Bashir, Syed Raza, Shaina Raza, Veysel Kocaman, and Urooj Qamar. "Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach." Viruses 14, no. 12 (December 11, 2022): 2761. http://dx.doi.org/10.3390/v14122761.

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The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics.

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Tavabi, Nazgol, Mallika Singh, James Pruneski, and Ata M. Kiapour. "Systematic evaluation of common natural language processing techniques to codify clinical notes." PLOS ONE 19, no. 3 (March 7, 2024): e0298892. http://dx.doi.org/10.1371/journal.pone.0298892.

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Proper codification of medical diagnoses and procedures is essential for optimized health care management, quality improvement, research, and reimbursement tasks within large healthcare systems. Assignment of diagnostic or procedure codes is a tedious manual process, often prone to human error. Natural Language Processing (NLP) has been suggested to facilitate this manual codification process. Yet, little is known on best practices to utilize NLP for such applications. With Large Language Models (LLMs) becoming more ubiquitous in daily life, it is critical to remember, not every task requires that level of resource and effort. Here we comprehensively assessed the performance of common NLP techniques to predict current procedural terminology (CPT) from operative notes. CPT codes are commonly used to track surgical procedures and interventions and are the primary means for reimbursement. Our analysis of 100 most common musculoskeletal CPT codes suggest that traditional approaches can outperform more resource intensive approaches like BERT significantly (P-value = 4.4e-17) with average AUROC of 0.96 and accuracy of 0.97, in addition to providing interpretability which can be very helpful and even crucial in the clinical domain. We also proposed a complexity measure to quantify the complexity of a classification task and how this measure could influence the effect of dataset size on model’s performance. Finally, we provide preliminary evidence that NLP can help minimize the codification error, including mislabeling due to human error.

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Gökgöl, Merve, and Zeynep Orhan. "OP41 Intercultural Medical Decision Support System Using Natural Language Processing (NLP)." International Journal of Technology Assessment in Health Care 35, S1 (2019): 10. http://dx.doi.org/10.1017/s0266462319001090.

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IntroductionThis study aimed to reach patients using different languages while providing an opportunity to enter symptoms in their everyday language text besides medical expressions of symptoms.MethodologyNamed entity recognition (NER) techniques, based on natural language processing (NLP), were applied to develop a language independent predictive model. The research was based on extracting symptoms entered to the system by patient using NER method of NLP. In order to implement the system, python was used while pre-processing the data and string similarity function was used to estimate similarity with disease symptoms. Two sets were used for classification, one including only symptoms, and the other the matching diseases. Four thousand two hundred and eighty different symptoms were processed for the corresponding 880 diseases.ResultsEach user symptom had a similarity score for each symptom in all diseases. Top N results with highest similarities were chosen from this list. The final N results are matched with diseases. According to these results, matched diseases were ordered in terms of the percentage of matched symptoms in the disease's symptoms. Extracted terms were implied as an input of the model and analysed for a matching diagnosis where an accuracy of 83 percent was accomplished when it is tested and compared using Mayo Clinic data for specific foreign languages other than English.ConclusionThis language independent online diagnostic tool is a solution for both personal and clinical use and provides maintainable, updatable and more reliable diagnostics. This tool is particularly relevant today, with global mobility growing at a rate faster than the world`s population. We aim to upgrade the system by adding speech recognition and engaging it with the background (if available, electronic health records) of the patient.

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Journal articles on the topic 'Clinical Natural Language Processing'

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